Methods For Rapid Identification Of Pathogens In Humans And Animals

ABSTRACT

The present invention provides methods of: identifying pathogens in biological samples from humans and animals, resolving a plurality of etiologic agents present in samples obtained from humans and animals, determining detailed genetic information about such pathogens or etiologic agents, and rapid detection and identification of bioagents from environmental, clinical or other samples.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application: 1) is a continuation-in-part of U.S.application Ser. No. 10/323,233 filed Dec. 18, 2002; 2) is acontinuation-in-part of U.S. application Ser. No. 10/326,051 filed Dec.18, 2002; 3) is a continuation-in-part of U.S. application Ser. No.10/325,527 filed Dec. 18, 2002; 4) is a continuation-in-part of U.S.application Ser. No. 10/325,526 filed Dec. 18, 2002; 5) claims thebenefit of U.S. provisional application Ser. No. 60/431,319 filed Dec.6, 2002; 6) claims the benefit of U.S. provisional application Ser. No.60/443,443 filed Jan. 29, 2003; 7) claims the benefit of U.S.provisional application Ser. No. 60/443,788 filed Jan. 30, 2003; 8)claims the benefit of U.S. provisional application Ser. No. 60/447,529filed Feb. 14, 2003; and 9) claims the benefit of U.S. provisionalapplication Ser. No. 60/501,926 filed Sep. 11, 2003, each of which isincorporated herein by reference in its entirety.

STATEMENT OF GOVERNMENT SUPPORT

This invention was made with United States government support underDARPA/SPO contract BAA00-09. The United States government may havecertain rights in the invention.

FIELD OF THE INVENTION

The present invention relates generally to clinical applications ofdirected to the identification of pathogens in biological samples fromhumans and animals. The present invention is also directed to theresolution of a plurality of etiologic agents present in samplesobtained from humans and animals. The invention is further directed tothe determination of detailed genetic information about such pathogensor etiologic agents.

The identification of the bioagent is important for determining a propercourse of treatment and/or eradication of the bioagent in such cases asbiological warfare and natural infections. Furthermore, thedetermination of the geographic origin of a selected bioagent willfacilitate the identification of potential criminal identity. Thepresent invention also relates to methods for rapid detection andidentification of bioagents from environmental, clinical or othersamples. The methods provide for detection and characterization of aunique base composition signature (BCS) from any bioagent, includingbacteria and viruses. The unique BCS is used to rapidly identify thebioagent.

BACKGROUND OF THE INVENTION

In the United States, hospitals report well over 5 million cases ofrecognized infectious disease-related illnesses annually. Significantlygreater numbers remain undetected, both in the inpatient and communitysetting, resulting in substantial morbidity and mortality. Criticalintervention for infectious disease relies on rapid, sensitive andspecific detection of the offending pathogen, and is central to themission of microbiology laboratories at medical centers. Unfortunately,despite the recognition that outcomes from infectious illnesses aredirectly associated with time to pathogen recognition, as well asaccurate identification of the class and species of microbe, and abilityto identify the presence of drug resistance isolates, conventionalhospital laboratories often remain encumbered by traditional slowmulti-step culture based assays. Other limitations of the conventionallaboratory which have become increasingly apparent include: extremelyprolonged wait-times for pathogens with long generation time (up toseveral weeks); requirements for additional testing and wait times forspeciation and identification of antimicrobial resistance; diminishedtest sensitivity for patients who have received antibiotics; andabsolute inability to culture certain pathogens in disease statesassociated with microbial infection.

For more than a decade, molecular testing has been heralded as thediagnostic tool for the new millennium, whose ultimate potential couldinclude forced obsolescence of traditional hospital laboratories.However, despite the fact that significant advances in clinicalapplication of PCR techniques have occurred, the practicing physicianstill relies principally on standard techniques. A brief discussion ofseveral existing applications of PCR in the hospital-based settingfollows.

Generally speaking molecular diagnostics have been championed foridentifying organisms that cannot be grown in vitro, or in instanceswhere existing culture techniques are insensitive and/or requireprolonged incubation times. PCR-based diagnostics have been successfullydeveloped for a wide variety of microbes. Application to the clinicalarena has met with variable success, with only a few assays achievingacceptance and utility.

One of the earliest, and perhaps most widely recognized applications ofPCR for clinical practice is in detection of Mycobacterium tuberculosis.Clinical characteristics favoring development of a nonculture-based testfor tuberculosis include week to month long delays associated withstandard testing, occurrence of drug-resistant isolates and publichealth imperatives associated with recognition, isolation and treatment.Although frequently used as a diagnostic adjunctive, practical androutine clinical application of PCR remains problematic due tosignificant inter-laboratory variation in sensitivity, and inadequatespecificity for use in low prevalence populations, requiring furtherdevelopment at the technical level. Recent advances in the laboratorysuggest that identification of drug resistant isolates by amplificationof mutations associated with specific antibiotic resistance (e.g., rpoBgene in rifampin resistant strains) may be forthcoming for clinical use,although widespread application will require extensive clinicalvalidation.

One diagnostic assay, which has gained widespread acceptance, is for C.trachomatis. Conventional detection systems are limiting due toinadequate sensitivity and specificity (direct immunofluorescence orenzyme immunoassay) or the requirement for specialized culturefacilities, due to the fastidious characteristics of this microbe.Laboratory development, followed by widespread clinical validationtesting in a variety of acute and nonacute care settings havedemonstrated excellent sensitivity (90-100%) and specificity (97%) ofthe PCR assay leading to its commercial development. Proven efficacy ofthe PCR assay from both genital and urine sampling, have resulted in itsapplication to a variety of clinical setting, most recently includingroutine screening of patients considered at risk.

While the full potential for PCR diagnostics to provide rapid andcritical information to physicians faced with difficultclinical-decisions has yet to be realized, one recently developed assayprovides an example of the promise of this evolving technology.Distinguishing life-threatening causes of fever from more benign causesin children is a fundamental clinical dilemma faced by clinicians,particularly when infections of the central nervous system are beingconsidered. Bacterial causes of meningitis can be highly aggressive, butgenerally cannot be differentiated on a clinical basis from asepticmeningitis, which is a relatively benign condition that can be managedon an outpatient basis. Existing blood culture methods often takeseveral days to turn positive, and are often confounded by poorsensitivity or false-negative findings in patients receiving empiricantimicrobials. Testing and application of a PCR assay for enteroviralmeningitis has been found to be highly sensitive. With reporting ofresults within 1 day, preliminary clinical trials have shown significantreductions in hospital costs, due to decreased duration of hospitalstays and reduction in antibiotic therapy. Other viral PCR assays, nowroutinely available include those for herpes simplex virus,cytomegalovirus, hepatitis and HIV. Each has a demonstrated cost savingsrole in clinical practice, including detection of otherwise difficult todiagnose infections and newly realized capacity to monitor progressionof disease and response to therapy, vital in the management of chronicinfectious diseases.

The concept of a universal detection system has been forwarded foridentification of bacterial pathogens, and speaks most directly to thepossible clinical implications of a broad-based screening tool forclinical use. Exploiting the existence of highly conserved regions ofDNA common to all bacterial species in a PCR assay would empowerphysicians to rapidly identify the presence of bacteremia, which wouldprofoundly impact patient care. Previous empiric decision making couldbe abandoned in favor of educated practice, allowing appropriate andexpeditious decision-making regarding need for antibiotic therapy andhospitalization.

Experimental work using the conserved features of the 16S rRNA common toalmost all bacterial species, is an area of active investigation.Hospital test sites have focused on “high yield” clinical settings whereexpeditious identification of the presence of systemic bacterialinfection has immediate high morbidity and mortality consequences.Notable clinical infections have included evaluation of febrile infantsat risk for sepsis, detection of bacteremia in febrile neutropeniccancer patients, and examination of critically ill patients in theintensive care unit. While several of these studies have reportedpromising results (with sensitivity and specificity well over 90%),significant technical difficulties (described below) remain, and haveprevented general acceptance of this assay in clinics and hospitals(which remain dependent on standard blood culture methodologies). Eventhe revolutionary advances of real-time PCR technique, which offers aquantitative more reproducible and technically simpler system, remainsencumbered by inherent technical limitations of the PCR assay.

The principle shortcomings of applying PCR assays to the clinicalsetting include: inability to eliminate background DNA contamination;interference with the PCR amplification by substrates present in thereaction; and limited capacity to provide rapid reliable speciation,antibiotic resistance and subtype identification. Some laboratories haverecently made progress in identifying and removing inhibitors; howeverbackground contamination remains problematic, and methods directedtowards eliminating exogenous sources of DNA report significantdiminution in assay sensitivity. Finally, while product identificationand detailed characterization has been achieved using sequencingtechniques, these approaches are laborious and time-intensive thusdetracting from its clinical applicability.

Rapid and definitive microbial identification is desirable for a varietyof industrial, medical, environmental, quality, and research reasons.Traditionally, the microbiology laboratory has functioned to identifythe etiologic agents of infectious diseases through direct examinationand culture of specimens. Since the mid-1980s, researchers haverepeatedly demonstrated the practical utility of molecular biologytechniques, many of which form the basis of clinical diagnostic assays.Some of these techniques include nucleic acid hybridization analysis,restriction enzyme analysis, genetic sequence analysis, and separationand purification of nucleic acids (See, e.g., J. Sambrook, E. F.Fritsch, and T. Maniatis, Molecular Cloning: A Laboratory Manual, 2ndEd., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.,1989). These procedures, in general, are time-consuming and tedious.Another option is the polymerase chain reaction (PCR) or otheramplification procedure that amplifies a specific target DNA sequencebased on the flanking primers used. Finally, detection and data analysisconvert the hybridization event into an analytical result.

Other not yet fully realized applications of PCR for clinical medicineis the identification of infectious causes of disease previouslydescribed as idiopathic (e.g. Bartonella henselae in bacillaryangiomatosis, and Tropheryma whippellii as the uncultured bacillusassociated with Whipple's disease). Further, recent epidemiologicalstudies which suggest a strong association between Chlamydia pneumoniaand coronary artery disease, serve as example of the possiblewidespread, yet undiscovered links between pathogen and host which mayultimately allow for new insights into pathogenesis and novel lifesustaining or saving therapeutics.

For the practicing clinician, PCR technology offers a yet unrealizedpotential for diagnostic omnipotence in the arena of infectious disease.A universal reliable infectious disease detection system would certainlybecome a fundamental tool in the evolving diagnostic armamentarium ofthe 21^(st) century clinician. For front line emergency physicians, orphysicians working in disaster settings, a quick universal detectionsystem, would allow for molecular triage and early aggressive targetedtherapy. Preliminary clinical studies using species specific probessuggest that implementing rapid testing in acute care setting isfeasible. Resources could thus be appropriately applied, and patientswith suspected infections could rapidly be risk stratified to thedifferent treatment settings, depending on the pathogen and virulence.Furthermore, links with data management systems, locally regionally andnationally, would allow for effective epidemiological surveillance, withobvious benefits for antibiotic selection and control of diseaseoutbreaks.

For the hospitalists, the ability to speculate and subtype would allowfor more precise decision-making regarding antimicrobial agents.Patients who are colonized with highly contagious pathogens could beappropriately isolated on entry into the medical setting without delay.Targeted therapy will diminish development of antibiotic resistance.Furthermore, identification of the genetic basis of antibiotic resistantstrains would permit precise pharmacologic intervention. Both physicianand patient would benefit with less need for repetitive testing andelimination of wait times for test results.

It is certain that the individual patient will benefit directly fromthis approach. Patients with unrecognized or difficult to diagnoseinfections would be identified and treated promptly. There will bereduced need for prolonged inpatient stays, with resultant decreases inintrogenic events.

Mass spectrometry provides detailed information about the moleculesbeing analyzed, including high mass accuracy. It is also a process thatcan be easily automated. Low-resolution MS may be unreliable when usedto detect some known agents, if their spectral lines are sufficientlyweak or sufficiently close to those from other living organisms in thesample. DNA chips with specific probes can only determine the presenceor absence of specifically anticipated organisms. Because there arehundreds of thousands of species of benign bacteria, some very similarin sequence to threat organisms, even arrays with 10,000 probes lack thebreadth needed to detect a particular organism.

Antibodies face more severe diversity limitations than arrays. Ifantibodies are designed against highly conserved targets to increasediversity, the false alarm problem will dominate, again because threatorganisms are very similar to benign ones. Antibodies are only capableof detecting known agents in relatively uncluttered environments.

Several groups have reported detection of PCR products using highresolution electrospray ionization-Fourier transform-ion cyclotronresonance mass spectrometry (ESI-FT-ICR MS). Accurate measurement ofexact mass combined with knowledge of the number of at least onenucleotide allowed calculation of the total base composition for PCRduplex products of approximately 100 base pairs. (Aaserud et al., J. Am.Soc. Mass Spec., 1996, 7, 1266-1269; Muddiman et al., Anal. Chem., 1997,69, 1543-1549; Wunschel et al., Anal. Chem., 1998, 70, 1203-1207;Muddiman et al., Rev. Anal. Chem., 1998, 17, 1-68). Electrosprayionization-Fourier transform-ion cyclotron resistance (ESI-FT-ICR) MSmay be used to determine the mass of double-stranded, 500 base-pair PCRproducts via the average molecular mass (Hurst et al., Rapid Commun.Mass Spec. 1996, 10, 377-382). Use of matrix-assisted laser desorptionionization-time of flight (MALDI-TOF) mass spectrometry forcharacterization of PCR products has been described. (Muddiman et al.,Rapid Commun. Mass Spec., 1999, 13, 1201-1204). However, the degradationof DNAs over about 75 nucleotides observed with MALDI limited theutility of this method.

U.S. Pat. No. 5,849,492 reports a method for retrieval ofphylogenetically informative DNA sequences which comprise searching fora highly divergent segment of genomic DNA surrounded by two highlyconserved segments, designing the universal primers for PCRamplification of the highly divergent region, amplifying the genomic DNAby PCR technique using universal primers, and then sequencing the geneto determine the identity of the organism.

U.S. Pat. No. 5,965,363 reports methods for screening nucleic acids forpolymorphisms by analyzing amplified target nucleic acids using massspectrometric techniques and to procedures for improving mass resolutionand mass accuracy of these methods.

WO 99/14375 reports methods, PCR primers and kits for use in analyzingpreselected DNA tandem nucleotide repeat alleles by mass spectrometry.

WO 98/12355 reports methods of determining the mass of a target nucleicacid by mass spectrometric analysis, by cleaving the target nucleic acidto reduce its length, making the target single-stranded and using MS todetermine the mass of the single-stranded shortened target. Alsoreported are methods of preparing a double-stranded target nucleic acidfor MS analysis comprising amplification of the target nucleic acid,binding one of the strands to a solid support, releasing the secondstrand and then releasing the first strand which is then analyzed by MS.Kits for target nucleic acid preparation are also provided.

PCT WO97/33000 reports methods for detecting mutations in a targetnucleic acid by nonrandomly fragmenting the target into a set ofsingle-stranded nonrandom length fragments and determining their massesby MS.

U.S. Pat. No. 5,605,798 reports a fast and highly accurate massspectrometer-based process for detecting the presence of a particularnucleic acid in a biological sample for diagnostic purposes.

WO 98/21066 reports processes for determining the sequence of aparticular target nucleic acid by mass spectrometry. Processes fordetecting a target nucleic acid present in a biological sample by PCRamplification and mass spectrometry detection are reported, as aremethods for detecting a target nucleic acid in a sample by amplifyingthe target with primers that contain restriction sites and tags,extending and cleaving the amplified nucleic acid, and detecting thepresence of extended product, wherein the presence of a DNA fragment ofa mass different from wild-type is indicative of a mutation. Methods ofsequencing a nucleic acid via mass spectrometry methods are alsoreported.

WO 97/37041, WO 99/31278 and U.S. Pat. No. 5,547,835 report methods ofsequencing nucleic acids using mass spectrometry. U.S. Pat. Nos.5,622,824, 5,872,003 and 5,691,141 report methods, systems and kits forexonuclease-mediated mass spectrometric sequencing.

Thus, there is a need for a method for bioagent detection andidentification which is both specific and rapid, and in which no nucleicacid sequencing is required. The present invention addresses this need.

SUMMARY OF THE INVENTION

The present invention is directed towards methods of identifying apathogen in a biological sample by obtaining nucleic acid from abiological sample, selecting at least one pair of intelligent primerswith the capability of amplification of nucleic acid of the pathogen,amplifying the nucleic acid with the primers to obtain at least oneamplification product, determining the molecular mass of at least oneamplification product from which the pathogen is identified. Further,this invention is directed to methods of epidemic surveillance. Byidentifying a pathogen from samples acquired from a plurality ofgeographic locations, the spread of the pathogen to a given geographiclocation can be determined.

The present invention is also directed to methods of diagnosis of aplurality of etiologic agents of disease in an individual by obtaining abiological sample from an individual, isolating nucleic acid from thebiological sample, selecting a plurality of amplification primers withthe capability of amplification of nucleic acid of a plurality ofetiologic agents of disease, amplifying the nucleic acid with aplurality of primers to obtain a plurality of amplification productscorresponding to a plurality of etiologic agents, determining themolecular masses of the plurality of unique amplification products whichidentify the members of the plurality of etiologic agents.

The present invention is also directed to methods of in silico screeningof primer sets to be used in identification of a plurality of bioagentsby preparing a base composition probability cloud plot from a pluralityof base composition signatures of the plurality of bioagents generatedin silico, inspecting the base composition probability cloud plot foroverlap of clouds from different bioagents, and choosing primer setsbased on minimal overlap of the clouds.

The present invention is also directed to methods of predicting theidentity of a bioagent with a heretofore unknown base compositionsignature by preparing a base composition probability cloud plot from aplurality of base composition signatures of the plurality of bioagentswhich includes the heretofore unknown base composition, inspecting thebase composition probability cloud for overlap of the heretofore unknownbase composition with the cloud of a known bioagent such that overlappredicts that the identity of the bioagent with a heretofore unknownbase composition signature equals the identity of the known bioagent.

The present invention is also directed to methods for determining asubspecies characteristic for a given pathogen in a biological sample byidentifying the pathogen in a biological sample using broad range surveyprimers or division-wide primers, selecting at least one pair ofdrill-down primers to amplify nucleic acid segments which provide asubspecies characteristic about the pathogen, amplifying the nucleicacid segments to produce at least one drill-down amplification productand determining the base composition signature of the drill-downamplification product wherein the base composition signature provides asubspecies characteristic about the pathogen.

The present invention is also directed to methods of pharmacogeneticanalysis by obtaining a sample of genomic DNA from an individual,selecting a segment of the genomic DNA which provides pharmacogeneticinformation, using at least one pair of intelligent primers to producean amplification product which comprises the segment of genomic DNA anddetermining the base composition signature of the amplification product,wherein the base composition signature provides pharmacogeneticinformation about said individual.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1H and FIG. 2 are consensus diagrams that show examples ofconserved regions from 16S rRNA (FIGS. 1A-1, 1A-2, 1A-3, 1A-4, and1A-5), 23S rRNA (3′-half, FIGS. 1B, 1C, and 1D; 5′-half, FIGS. 1E-F),23S rRNA Domain I (FIG. 1G), 23S rRNA Domain IV (FIG. 1H) and 16S rRNADomain III (FIG. 2) which are suitable for use in the present invention.Lines with arrows are examples of regions to which intelligent primerpairs for PCR are designed. The label for each primer pair representsthe starting and ending base number of the amplified region on theconsensus diagram. Bases in capital letters are greater than 95%conserved; bases in lower case letters are 90-95% conserved, filledcircles are 80-90% conserved; and open circles are less than 80%conserved. The label for each primer pair represents the starting andending base number of the amplified region on the consensus diagram. Thenucleotide sequence of the 16S rRNA consensus sequence is SEQ ID NO:3and the nucleotide sequence of the 23S rRNA consensus sequence is SEQ IDNO:4.

FIG. 2 shows a typical primer amplified region from the 16S rRNA DomainIII shown in FIG. 1A-1.

FIG. 3 is a schematic diagram showing conserved regions in RNase P.Bases in capital letters are greater than 90% conserved; bases in lowercase letters are 80-90% conserved; filled circles designate bases whichare 70-80% conserved; and open circles designate bases that are lessthan 70% conserved.

FIG. 4 is a schematic diagram of base composition signaturedetermination using nucleotide analog “tags” to determine basecomposition signatures.

FIG. 5 shows the deconvoluted mass spectra of a Bacillus anthracisregion with and without the mass tag phosphorothioate A (A*). The twospectra differ in that the measured molecular weight of the masstag-containing sequence is greater than the unmodified sequence.

FIG. 6 shows base composition signature (BCS) spectra from PCR productsfrom Staphylococcus aureus (S. aureus 16S_(—)1337F) and Bacillusanthracis (B. anthr. 16S_(—)1337F), amplified using the same primers.The two strands differ by only two (AT->CG) substitutions and areclearly distinguished on the basis of their BCS.

FIG. 7 shows that a single difference between two sequences (A 14 in B.anthracis vs. A15 in B. cereus) can be easily detected using ESI-TOFmass spectrometry.

FIG. 8 is an ESI-TOF of Bacillus anthracis spore coat protein sspE 56mer plus calibrant. The signals unambiguously identify B. anthracisversus other Bacillus species.

FIG. 9 is an ESI-TOF of a B. anthracis synthetic 16S_(—)1228 duplex(reverse and forward strands). The technique easily distinguishesbetween the forward and reverse strands.

FIG. 10 is an ESI-FTICR-MS of a synthetic B. anthracis 16S_(—)1337 46base pair duplex.

FIG. 11 is an ESI-TOF-MS of a 56 mer oligonucleotide (3 scans) from theB. anthracis saspB gene with an internal mass standard. The internalmass standards are designated by asterisks.

FIG. 12 is an ESI-TOF-MS of an internal standard with 5 mM TBA-TFAbuffer showing that charge stripping with tributylammoniumtrifluoroacetate reduces the most abundant charge state from [M-8H+]8−to [M-3H+]3−.

FIG. 13 is a portion of a secondary structure defining databaseaccording to one embodiment of the present invention, where two examplesof selected sequences are displayed graphically thereunder.

FIG. 14 is a three dimensional graph demonstrating the grouping ofsample molecular weight according to species.

FIG. 15 is a three dimensional graph demonstrating the grouping ofsample molecular weights according to species of virus and mammalinfected.

FIG. 16 is a three dimensional graph demonstrating the grouping ofsample molecular weights according to species of virus, andanimal-origin of infectious agent.

FIG. 17 is a figure depicting how a typical triangulation method of thepresent invention provides for the identification of an unknown bioagentwithout prior knowledge of the unknown agent. The use of differentprimer sets to distinguish and identify the unknown is also depicted asprimer sets I, II and III within this figure. A three-dimensional graphdepicts all of bioagent space (170), including the unknown bioagent,which after use of primer set I (171) according to a method according tothe present invention further differentiates and classifies bioagentsaccording to major classifications (176) which, upon further analysisusing primer set II (172) differentiates the unknown agent (177) fromother, known agents (173) and finally, the use of a third primer set(175) further specifies subgroups within the family of the unknown(174).

FIG. 18 shows a representative base composition probability cloud for aregion of the RNA polymerase B gene from a cluster of enterobacteria.The dark spheres represent the actual base composition of the organisms.The lighter spheres represent the transitions among base compositionsobserved in different isolates of the same species of organism.

FIG. 19 shows resolution of enterobacteriae members with primerstargeting RNA polymerase B (rpoB). A single pair of primers targeting ahyper-variable region within rpoB was sufficient to resolve most membersof this group at the genus level (Salmonella from Escherichia fromYersinia) as well as the species/strain level (E. coli K12 from O157).All organisms with the exception of Y. pestis were tested in the lab andthe measured base counts (shown with arrow) matched the predictions inevery case.

FIG. 20 shows detection of S. aureus in blood. Spectra on the rightindicate signals corresponding to S. aureus detection in spiked wells A1and A4 with no detection in control wells A2 and A3.

FIG. 21 shows a representative base composition distribution of humanadenovirus strain types for a single primer pair region on the hexongene. The circles represent different adenovirus sequences in ourdatabase that were used for primer design. Measurement of masses andbase counts for each of the unknown samples A, B, C and D matched one ormore of the known groups of adenoviruses.

FIG. 22 shows a representative broad range survey/drill-down process asapplied to emm-typing of streptococcus pyogenes (Group A Streptococcus:GAS). Genetic material is extracted (201) and amplified using broadrange survey primers (202). The amplification products are analyzed(203) to determine the presence and identity of bioagents at the specieslevel. If Streptococcus pyogenes is detected (204), the emm-typing“drill-down” primers are used to reexamine the extract to identify theemm-type of the sample (205). Different sets of drill down primers canbe employed to determine a subspecies characteristic for various strainsof various bioagents (206).

FIG. 23 shows a representative base composition distribution ofbioagents detected in throat swabs from military personnel using a broadrange primer pair directed to 16S rRNA.

FIG. 24 shows a representative deconvoluted ESI-FTICR spectra of the PCRproducts produced by the gtr primer for samples 12 (top) and 10 (bottom)corresponding to emm types 3 and 6, respectively. Accurate massmeasurements were obtained by using an internal mass standard andpost-calibrating each spectrum; the experimental mass measurementuncertainty on each strand is +0.035 Daltons (1 ppm). Unambiguous basecompositions of the amplicons were determined by calculating allputative base compositions of each stand within the measured mass (andmeasured mass uncertainty) and selecting complementary pairs within themass measurement uncertainty. In all cases there was only one basecomposition within 25 ppm. The measured mass difference of 15.985 Dabetween the strands shown on the left is in excellent agreement with thetheoretical mass difference of 15.994 Da expected for an A to Gsubstitution.

FIG. 25 shows representative results of the base composition analysis onthroat swab samples using the six primer pairs, 5′-emm gene sequencingand the MLST gene sequencing method of the present invention for anoutbreak of Streptococcus pyogenes (group A streptococcus; GAS) at amilitary training camp.

FIG. 26 shows: a) a representative ESI-FTICR mass spectrum of arestriction digest of a 986 bp region of the 16S ribosomal gene from E.coli K 12 digested with a mixture of BstNI, BsmFI, BfaI, and NcoI; b) adeconvoluted representation (neutral mass) of the above spectrum showingthe base compositions derived from accurate mass measurements of eachfragment; and c) a representative reconstructed restriction map showingcomplete base composition coverage for nucleotides 1-856. The Nco1 didnot cut.

FIG. 27 shows a representative base composition distribution ofpoxviruses for a single primer pair region on the DNA-dependentpolymerase B gene (DdDpB). The spheres represent different poxvirussequences that were used for primer design.

DESCRIPTION OF EMBODIMENTS

The present invention provides, inter alia, methods for detection andidentification of bioagents in an unbiased manner using “bioagentidentifying amplicons.” “Intelligent primers” are selected to hybridizeto conserved sequence regions of nucleic acids derived from a bioagentand which bracket variable sequence regions to yield a bioagentidentifying amplicon which can be amplified and which is amenable tomolecular mass determination. The molecular mass then provides a meansto uniquely identify the bioagent without a requirement for priorknowledge of the possible identity of the bioagent. The molecular massor corresponding “base composition signature” (BCS) of the amplificationproduct is then matched against a database of molecular masses or basecomposition signatures. Furthermore, the method can be applied to rapidparallel “multiplex” analyses, the results of which can be employed in atriangulation identification strategy. The present method provides rapidthroughput and does not require nucleic acid sequencing of the amplifiedtarget sequence for bioagent detection and identification.

In the context of this invention, a “bioagent” is any organism, cell, orvirus, living or dead, or a nucleic acid derived from such an organism,cell or virus. Examples of bioagents include, but are not limited, tocells (including, but not limited to, human clinical samples, bacterialcells and other pathogens) viruses, fungi, and protists, parasites, andpathogenicity markers (including, but not limited to, pathogenicityislands, antibiotic resistance genes, virulence factors, toxin genes andother bioregulating compounds). Samples may be alive or dead or in avegetative state (for example, vegetative bacteria or spores) and may beencapsulated or bioengineered. In the context of this invention, a“pathogen” is a bioagent that causes a disease or disorder.

Despite enormous biological diversity, all forms of life on earth sharesets of essential, common features in their genomes. Bacteria, forexample have highly conserved sequences in a variety of locations ontheir genomes. Most notable is the universally conserved region of theribosome, but there are also conserved elements in other non-codingRNAs, including RNAse P and the signal recognition particle (SRP) amongothers. Bacteria have a common set of absolutely required genes. About250 genes are present in all bacterial species (Mushegian et al., Proc.Natl. Acad. Sci. U.S.A., 1996, 93, 10268; and Fraser et al., Science,1995, 270, 397), including tiny genomes like Mycoplasma, Ureaplasma andRickettsia. These genes encode proteins involved in translation,replication, recombination and repair, transcription, nucleotidemetabolism, amino acid metabolism, lipid metabolism, energy generation,uptake, secretion and the like. Examples of these proteins are DNApolymerase III beta, elongation factor TU, heat shock protein groEL, RNApolymerase beta, phosphoglycerate kinase, NADH dehydrogenase, DNAligase, DNA topoisomerase and elongation factor G. Operons can also betargeted using the present method. One example of an operon is the bfpoperon from enteropathogenic E. coli. Multiple core chromosomal genescan be used to classify bacteria at a genus or genus species level todetermine if an organism has threat potential. The methods can also beused to detect pathogenicity markers (plasmid or chromosomal) andantibiotic resistance genes to confirm the threat potential of anorganism and to direct countermeasures.

Since genetic data provide the underlying basis for identification ofbioagents by the methods of the present invention, it is prudent toselect segments of nucleic acids which ideally provide enoughvariability to distinguish each individual bioagent and whose molecularmass is amenable to molecular mass determination. In one embodiment ofthe present invention, at least one polynucleotide segment is amplifiedto facilitate detection and analysis in the process of identifying thebioagent. Thus, the nucleic acid segments that provide enoughvariability to distinguish each individual bioagent and whose molecularmasses are amenable to molecular mass determination are herein describedas “bioagent identifying amplicons.” The term “amplicon” as used herein,refers to a segment of a polynucleotide which is amplified in anamplification reaction. In some embodiments of the present invention,bioagent identifying amplicons comprise from about 45 to about 150nucleobases (i.e. from about 45 to about 150 linked nucleosides). One ofordinary skill in the art will appreciate that the invention embodiescompounds of 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95,96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110,111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124,125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, and 150nucleobases in length.

As used herein, “intelligent primers” are primers that are designed tobind to highly conserved sequence regions that flank an interveningvariable region and yield amplification products which ideally provideenough variability to distinguish each individual bioagent, and whichare amenable to molecular mass analysis. By the term “highly conserved,”it is meant that the sequence regions exhibit between about 80-100%, orbetween about 90-100%, or between about 95-100% identity. The molecularmass of a given amplification product provides a means of identifyingthe bioagent from which it was obtained, due to the variability of thevariable region. Thus, design of intelligent primers involves selectionof a variable region with appropriate variability to resolve theidentity of a particular bioagent. It is the combination of the portionof the bioagent nucleic acid molecule sequence to which the intelligentprimers hybridize and the intervening variable region that makes up thebioagent identifying amplicon. Alternately, it is the interveningvariable region by itself that makes up the bioagent identifyingamplicon.

It is understood in the art that the sequence of a primer need not be100% complementary to that of its target nucleic acid to be specificallyhybridizable. Moreover, a primer may hybridize over one or more segmentssuch that intervening or adjacent segments are not involved in thehybridization event (e.g., a loop structure or hairpin structure). Theprimers of the present invention can comprise at least 70%, at least75%, at least 80%, at least 85%, at least 90%, at least 95%, or at least99% sequence complementarity to the target region within the highlyconserved region to which they are targeted. For example, an intelligentprimer wherein 18 of 20 nucleobases are complementary to a highlyconserved region would represent 90 percent complementarity to thehighly conserved region. In this example, the remaining noncomplementarynucleobases may be clustered or interspersed with complementarynucleobases and need not be contiguous to each other or to complementarynucleobases. As such, a primer which is 18 nucleobases in length having4 (four) noncomplementary nucleobases which are flanked by two regionsof complete complementarity with the highly conserved region would have77.8% overall complementarity with the highly conserved region and wouldthus fall within the scope of the present invention. Percentcomplementarity of a primer with a region of a target nucleic acid canbe determined routinely using BLAST programs (basic local alignmentsearch tools) and PowerBLAST programs known in the art (Altschul et al.,J. Mol. Biol., 1990, 215, 403-410; Zhang and Madden, Genome Res., 1997,7, 649-656).

Percent homology, sequence identity or complementarity, can bedetermined by, for example, the Gap program (Wisconsin Sequence AnalysisPackage, Version 8 for Unix, Genetics Computer Group, UniversityResearch Park, Madison Wis.), using default settings, which uses thealgorithm of Smith and Waterman (Adv. Appl. Math., 1981, 2, 482-489). Insome embodiments, complementarity of intelligent primers, is betweenabout 70% and about 80%. In other embodiments, homology, sequenceidentity or complementarity, is between about 80% and about 90%. In yetother embodiments, homology, sequence identity or complementarity, isabout 90%, about 92%, about 94%, about 95%, about 96%, about 97%, about98%, about 99% or about 100%.

The intelligent primers of this invention comprise from about 12 toabout 35 nucleobases (i.e. from about 12 to about 35 linkednucleosides). One of ordinary skill in the art will appreciate that theinvention embodies compounds of 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, or 35 nucleobases inlength.

One having skill in the art armed with the preferred bioagentidentifying amplicons defined by the primers illustrated herein will beable, without undue experimentation, to identify additional intelligentprimers.

In one embodiment, the bioagent identifying amplicon is a portion of aribosomal RNA (rRNA) gene sequence. With the complete sequences of manyof the smallest microbial genomes now available, it is possible toidentify a set of genes that defines “minimal life” and identifycomposition signatures that uniquely identify each gene and organism.Genes that encode core life functions such as DNA replication,transcription, ribosome structure, translation, and transport aredistributed broadly in the bacterial genome and are suitable regions forselection of bioagent identifying amplicons. Ribosomal RNA (rRNA) genescomprise regions that provide useful base composition signatures. Likemany genes involved in core life functions, rRNA genes contain sequencesthat are extraordinarily conserved across bacterial domains interspersedwith regions of high variability that are more specific to each species.The variable regions can be utilized to build a database of basecomposition signatures. The strategy involves creating a structure-basedalignment of sequences of the small (16S) and the large (23S) subunitsof the rRNA genes. For example, there are currently over 13,000sequences in the ribosomal RNA database that has been created andmaintained by Robin Gutell, University of Texas at Austin, and ispublicly available on the Institute for Cellular and Molecular Biologyweb page on the world wide web of the Internet at, for example,“rna.icmb.utexas.edu/.” There is also a publicly available rRNA databasecreated and maintained by the University of Antwerp, Belgium on theworld wide web of the Internet at, for example, “rrna.uia.ac.be.”

These databases have been analyzed to determine regions that are usefulas bioagent identifying amplicons. The characteristics of such regionsinclude: a) between about 80 and 100%, or greater than about 95%identity among species of the particular bioagent of interest, ofupstream and downstream nucleotide sequences which serve as sequenceamplification primer sites; b) an intervening variable region whichexhibits no greater than about 5% identity among species; and c) aseparation of between about 30 and 1000 nucleotides, or no more thanabout 50-250 nucleotides, or no more than about 60-100 nucleotides,between the conserved regions.

As a non-limiting example, for identification of Bacillus species, theconserved sequence regions of the chosen bioagent identifying ampliconmust be highly conserved among all Bacillus species while the variableregion of the bioagent identifying amplicon is sufficiently variablesuch that the molecular masses of the amplification products of allspecies of Bacillus are distinguishable.

Bioagent identifying amplicons amenable to molecular mass determinationare either of a length, size or mass compatible with the particular modeof molecular mass determination or compatible with a means of providinga predictable fragmentation pattern in order to obtain predictablefragments of a length compatible with the particular mode of molecularmass determination. Such means of providing a predictable fragmentationpattern of an amplification product include, but are not limited to,cleavage with restriction enzymes or cleavage primers, for example.

Identification of bioagents can be accomplished at different levelsusing intelligent primers suited to resolution of each individual levelof identification. “Broad range survey” intelligent primers are designedwith the objective of identifying a bioagent as a member of a particulardivision of bioagents. A “bioagent division” is defined as group ofbioagents above the species level and includes but is not limited to:orders, families, classes, clades, genera or other such groupings ofbioagents above the species level. As a non-limiting example, members ofthe Bacillus/Clostridia group or gamma-proteobacteria group may beidentified as such by employing broad range survey intelligent primerssuch as primers that target 16S or 23S ribosomal RNA.

In some embodiments, broad range survey intelligent primers are capableof identification of bioagents at the species level. One main advantageof the detection methods of the present invention is that the broadrange survey intelligent primers need not be specific for a particularbacterial species, or even genus, such as Bacillus or Streptomyces.Instead, the primers recognize highly conserved regions across hundredsof bacterial species including, but not limited to, the speciesdescribed herein. Thus, the same broad range survey intelligent primerpair can be used to identify any desired bacterium because it will bindto the conserved regions that flank a variable region specific to asingle species, or common to several bacterial species, allowingunbiased nucleic acid amplification of the intervening sequence anddetermination of its molecular weight and base composition. For example,the 16S_(—)971-1062, 16S_(—)1228-1310 and 16S_(—)1100-1188 regions are98-99% conserved in about 900 species of bacteria (16S=16S rRNA, numbersindicate nucleotide position). In one embodiment of the presentinvention, primers used in the present method bind to one or more ofthese regions or portions thereof.

Due to their overall conservation, the flanking rRNA primer sequencesserve as good intelligent primer binding sites to amplify the nucleicacid region of interest for most, if not all, bacterial species. Theintervening region between the sets of primers varies in length and/orcomposition, and thus provides a unique base composition signature.Examples of intelligent primers that amplify regions of the 16S and 23SrRNA are shown in FIGS. 1A-11H. A typical primer amplified region in 16SrRNA is shown in FIG. 2. The arrows represent primers that bind tohighly conserved regions that flank a variable region in 16S rRNA domainIII. The amplified region is the stem-loop structure under “1100-1188.”It is advantageous to design the broad range survey intelligent primersto minimize the number of primers required for the analysis, and toallow detection of multiple members of a bioagent division using asingle pair of primers. The advantage of using broad range surveyintelligent primers is that once a bioagent is broadly identified, theprocess of further identification at species and sub-species levels isfacilitated by directing the choice of additional intelligent primers.

“Division-wide” intelligent primers are designed with an objective ofidentifying a bioagent at the species level. As a non-limiting example,a Bacillus anthracis, Bacillus cereus and Bacillus thuringiensis can bedistinguished from each other using division-wide intelligent primers.Division-wide intelligent primers are not always required foridentification at the species level because broad range surveyintelligent primers may provide sufficient identification resolution toaccomplishing this identification objective.

“Drill-down” intelligent primers are designed with an objective ofidentifying a sub-species characteristic of a bioagent. A “sub-speciescharacteristic” is defined as a property imparted to a bioagent at thesub-species level of identification as a result of the presence orabsence of a particular segment of nucleic acid. Such sub-speciescharacteristics include, but are not limited to, strains, sub-types,pathogenicity markers such as antibiotic resistance genes, pathogenicityislands, toxin genes and virulence factors. Identification of suchsub-species characteristics is often critical for determining properclinical treatment of pathogen infections.

Chemical Modifications of Intelligent Primers

Ideally, intelligent primer hybridization sites are highly conserved inorder to facilitate the hybridization of the primer. In cases whereprimer hybridization is less efficient due to lower levels ofconservation of sequence, intelligent primers can be chemically modifiedto improve the efficiency of hybridization.

For example, because any variation (due to codon wobble in the 3^(rd)position) in these conserved regions among species is likely to occur inthe third position of a DNA triplet, oligonucleotide primers can bedesigned such that the nucleotide corresponding to this position is abase which can bind to more than one nucleotide, referred to herein as a“universal base.” For example, under this “wobble” pairing, inosine (I)binds to U, C or A; guanine (G) binds to U or C, and uridine (U) bindsto U or C. Other examples of universal bases include nitroindoles suchas 5-nitroindole or 3-nitropyrrole (Loakes et al., Nucleosides andNucleotides, 1995, 14, 1001-1003), the degenerate nucleotides dP or dK(Hill et al.), an acyclic nucleoside analog containing 5-nitroindazole(Van Aerschot et al., Nucleosides and Nucleotides, 1995, 14, 1053-1056)or the purine analog1-(2-deoxy-β-D-ribofuranosyl)-imidazole-4-carboxamide (Sala et al.,Nucl. Acids Res., 1996, 24, 3302-3306).

In another embodiment of the invention, to compensate for the somewhatweaker binding by the “wobble” base, the oligonucleotide primers aredesigned such that the first and second positions of each triplet areoccupied by nucleotide analogs which bind with greater affinity than theunmodified nucleotide. Examples of these analogs include, but are notlimited to, 2,6-diaminopurine which binds to thymine, propyne T whichbinds to adenine and propyne C and phenoxazines, including G-clamp,which binds to G. Propynylated pyrimidines are described in U.S. Pat.Nos. 5,645,985, 5,830,653 and 5,484,908, each of which is commonly ownedand incorporated herein by reference in its entirety. Propynylatedprimers are claimed in U.S. Ser. No. 10/294,203 which is also commonlyowned and incorporated herein by reference in entirety. Phenoxazines aredescribed in U.S. Pat. Nos. 5,502,177, 5,763,588, and 6,005,096, each ofwhich is incorporated herein by reference in its entirety. G-clamps aredescribed in U.S. Pat. Nos. 6,007,992 and 6,028,183, each of which isincorporated herein by reference in its entirety.

A theoretically ideal bioagent detector would identify, quantify, andreport the complete nucleic acid sequence of every bioagent that reachedthe sensor. The complete sequence of the nucleic acid component of apathogen would provide all relevant information about the threat,including its identity and the presence of drug-resistance orpathogenicity markers. This ideal has not yet been achieved. However,the present invention provides a straightforward strategy for obtaininginformation with the same practical value based on analysis of bioagentidentifying amplicons by molecular mass determination.

In some cases, a molecular mass of a given bioagent identifying ampliconalone does not provide enough resolution to unambiguously identify agiven bioagent. For example, the molecular mass of the bioagentidentifying amplicon obtained using the intelligent primer pair“16S_(—)971” would be 55622 Da for both E. coli and Salmonellatyphimurium. However, if additional intelligent primers are employed toanalyze additional bioagent identifying amplicons, a “triangulationidentification” process is enabled. For example, the “16S_(—)1100”intelligent primer pair yields molecular masses of 55009 and 55005 Dafor E. coli and Salmonella typhimurium, respectively. Furthermore, the“23S_(—)855” intelligent primer pair yields molecular masses of 42656and 42698 Da for E. coli and Salmonella typhimurium, respectively. Inthis basic example, the second and third intelligent primer pairsprovided the additional “fingerprinting” capability or resolution todistinguish between the two bioagents.

In another embodiment, the triangulation identification process ispursued by measuring signals from a plurality of bioagent identifyingamplicons selected within multiple core genes. This process is used toreduce false negative and false positive signals, and enablereconstruction of the origin of hybrid or otherwise engineeredbioagents. In this process, after identification of multiple core genes,alignments are created from nucleic acid sequence databases. Thealignments are then analyzed for regions of conservation and variation,and bioagent identifying amplicons are selected to distinguish bioagentsbased on specific genomic differences. For example, identification ofthe three part toxin genes typical of B. anthracis (Bowen et al., J.Appl. Microbiol., 1999, 87, 270-278) in the absence of the expectedsignatures from the B. anthracis genome would suggest a geneticengineering event.

The triangulation identification process can be pursued bycharacterization of bioagent identifying amplicons in a massivelyparallel fashion using the polymerase chain reaction (PCR), such asmultiplex PCR, and mass spectrometric (MS) methods. Sufficientquantities of nucleic acids should be present for detection of bioagentsby MS. A wide variety of techniques for preparing large amounts ofpurified nucleic acids or fragments thereof are well known to those ofskill in the art. PCR requires one or more pairs of oligonucleotideprimers that bind to regions which flank the target sequence(s) to beamplified. These primers prime synthesis of a different strand of DNAwith synthesis occurring in the direction of one primer towards theother primer. The primers, DNA to be amplified, a thermostable DNApolymerase (e.g. Taq polymerase), the four deoxynucleotidetriphosphates, and a buffer are combined to initiate DNA synthesis. Thesolution is denatured by heating, then cooled to allow annealing ofnewly added primer, followed by another round of DNA synthesis. Thisprocess is typically repeated for about 30 cycles, resulting inamplification of the target sequence.

Although the use of PCR is suitable, other nucleic acid amplificationtechniques may also be used, including ligase chain reaction (LCR) andstrand displacement amplification (SDA). The high-resolution MStechnique allows separation of bioagent spectral lines from backgroundspectral lines in highly cluttered environments.

In another embodiment, the detection scheme for the PCR productsgenerated from the bioagent(s) incorporates at least three features.First, the technique simultaneously detects and differentiates multiple(generally about 6-10) PCR products. Second, the technique provides amolecular mass that uniquely identifies the bioagent from the possibleprimer sites. Finally, the detection technique is rapid, allowingmultiple PCR reactions to be run in parallel.

Mass spectrometry (MS)-based detection of PCR products provides a meansfor determination of BCS that has several advantages. MS isintrinsically a parallel detection scheme without the need forradioactive or fluorescent labels, since every amplification product isidentified by its molecular mass. The current state of the art in massspectrometry is such that less than femtomole quantities of material canbe readily analyzed to afford information about the molecular contentsof the sample. An accurate assessment of the molecular mass of thematerial can be quickly obtained, irrespective of whether the molecularweight of the sample is several hundred, or in excess of one hundredthousand atomic mass units (amu) or Daltons. Intact molecular ions canbe generated from amplification products using one of a variety ofionization techniques to convert the sample to gas phase. Theseionization methods include, but are not limited to, electrosprayionization (ES), matrix-assisted laser desorption ionization (MALDI) andfast atom bombardment (FAB). For example, MALDI of nucleic acids, alongwith examples of matrices for use in MALDI of nucleic acids, aredescribed in WO 98/54751 (Genetrace, Inc.).

In some embodiments, large DNAs and RNAs, or large amplificationproducts therefrom, can be digested with restriction endonucleases priorto ionization. Thus, for example, an amplification product that was 10kDa could be digested with a series of restriction endonucleases toproduce a panel of, for example, 100 Da fragments. Restrictionendonucleases and their sites of action are well known to the skilledartisan. In this manner, mass spectrometry can be performed for thepurposes of restriction mapping.

Upon ionization, several peaks are observed from one sample due to theformation of ions with different charges. Averaging the multiplereadings of molecular mass obtained from a single mass spectrum affordsan estimate of molecular mass of the bioagent. Electrospray ionizationmass spectrometry (ESI-MS) is particularly useful for very highmolecular weight polymers such as proteins and nucleic acids havingmolecular weights greater than 10 kDa, since it yields a distribution ofmultiply-charged molecules of the sample without causing a significantamount of fragmentation.

The mass detectors used in the methods of the present invention include,but are not limited to, Fourier transform ion cyclotron resonance massspectrometry (FT-ICR-MS), ion trap, quadrupole, magnetic sector, time offlight (TOF), Q-TOF, and triple quadrupole.

In general, the mass spectrometric techniques which can be used in thepresent invention include, but are not limited to, tandem massspectrometry, infrared multiphoton dissociation and pyrolytic gaschromatography mass spectrometry (PGC-MS). In one embodiment of theinvention, the bioagent detection system operates continually inbioagent detection mode using pyrolytic GC-MS without PCR for rapiddetection of increases in biomass (for example, increases in fecalcontamination of drinking water or of germ warfare agents). To achieveminimal latency, a continuous sample stream flows directly into thePGC-MS combustion chamber. When an increase in biomass is detected, aPCR process is automatically initiated. Bioagent presence produceselevated levels of large molecular fragments from, for example, about100-7,000 Da which are observed in the PGC-MS spectrum. The observedmass spectrum is compared to a threshold level and when levels ofbiomass are determined to exceed a predetermined threshold, the bioagentclassification process described hereinabove (combining PCR and MS, suchas FT-ICR MS) is initiated. Optionally, alarms or other processes(halting ventilation flow, physical isolation) are also initiated bythis detected biomass level.

The accurate measurement of molecular mass for large DNAs is limited bythe adduction of cations from the PCR reaction to each strand,resolution of the isotopic peaks from natural abundance ¹³C and ¹⁵Nisotopes, and assignment of the charge state for any ion. The cationsare removed by in-line dialysis using a flow-through chip that bringsthe solution containing the PCR products into contact with a solutioncontaining ammonium acetate in the presence of an electric fieldgradient orthogonal to the flow. The latter two problems are addressedby operating with a resolving power of >100,000 and by incorporatingisotopically depleted nucleotide triphosphates into the DNA. Theresolving power of the instrument is also a consideration. At aresolving power of 10,000, the modeled signal from the [M-14H+]¹⁴⁻charge state of an 84 mer PCR product is poorly characterized andassignment of the charge state or exact mass is impossible. At aresolving power of 33,000, the peaks from the individual isotopiccomponents are visible. At a resolving power of 100,000, the isotopicpeaks are resolved to the baseline and assignment of the charge statefor the ion is straightforward. The [¹³C,¹⁵N]-depleted triphosphates areobtained, for example, by growing microorganisms on depleted media andharvesting the nucleotides (Batey et al., Nucl. Acids Res., 1992, 20,4515-4523).

While mass measurements of intact nucleic acid regions are believed tobe adequate to determine most bioagents, tandem mass spectrometry(MS^(n)) techniques may provide more definitive information pertainingto molecular identity or sequence. Tandem MS involves the coupled use oftwo or more stages of mass analysis where both the separation anddetection steps are based on mass spectrometry. The first stage is usedto select an ion or component of a sample from which further structuralinformation is to be obtained. The selected ion is then fragmentedusing, e.g., blackbody irradiation, infrared multiphoton dissociation,or collisional activation. For example, ions generated by electrosprayionization (ESI) can be fragmented using IR multiphoton dissociation.This activation leads to dissociation of glycosidic bonds and thephosphate backbone, producing two series of fragment ions, called thew-series (having an intact 3′ terminus and a 5′ phosphate followinginternal cleavage) and the a-Base series (having an intact 5′ terminusand a 3′ furan).

The second stage of mass analysis is then used to detect and measure themass of these resulting fragments of product ions. Such ion selectionfollowed by fragmentation routines can be performed multiple times so asto essentially completely dissect the molecular sequence of a sample.

If there are two or more targets of similar molecular mass, or if asingle amplification reaction results in a product that has the samemass as two or more bioagent reference standards, they can bedistinguished by using mass-modifying “tags.” In this embodiment of theinvention, a nucleotide analog or “tag” is incorporated duringamplification (e.g., a 5-(trifluoromethyl) deoxythymidine triphosphate)which has a different molecular weight than the unmodified base so as toimprove distinction of masses. Such tags are described in, for example,PCT WO97/33000, which is incorporated herein by reference in itsentirety. This further limits the number of possible base compositionsconsistent with any mass. For example, 5-(trifluoromethyl)deoxythymidinetriphosphate can be used in place of dTTP in a separate nucleic acidamplification reaction. Measurement of the mass shift between aconventional amplification product and the tagged product is used toquantitate the number of thymidine nucleotides in each of the singlestrands. Because the strands are complementary, the number of adenosinenucleotides in each strand is also determined.

In another amplification reaction, the number of G and C residues ineach strand is determined using, for example, the cytidine analog5-methylcytosine (5-meC) or propyne C. The combination of the A/Treaction and G/C reaction, followed by molecular weight determination,provides a unique base composition. This method is summarized in FIG. 4and Table 1.

TABLE 1 Total Base Base Total base Total base Double strand Singlestrand mass this info this info other comp. Top comp. Bottom Mass tagsequence Sequence strand strand strand strand strand T

mass T*ACGT*ACGT* T*ACGT*ACGT* 3x 3T 3A 3T 3A (T* − T) = x AT*GCAT*GCA2A 2T 2C 2G 2G 2C AT*GCAT*GCA 2x 2T 2A C

mass TAC*GTAC*GT TAC*GTAC*GT 2x 2C 2G (C* − C) = y ATGC*ATGC*AATGC*ATGC*A 2x 2C 2G

The mass tag phosphorothioate A (A*) was used to distinguish a Bacillusanthracis cluster. The B. anthracis (A₁₄G₉C₁₄T₉) had an average MW of14072.26, and the B. anthracis (A₁A*₁₃G₉C₁₄T₉) had an average molecularweight of 14281.11 and the phosphorothioate A had an average molecularweight of +16.06 as determined by ESI-TOF MS. The deconvoluted spectraare shown in FIG. 5.

In another example, assume the measured molecular masses of each strandare 30,000.115Da and 31,000.115 Da respectively, and the measured numberof dT and dA residues are (30,28) and (28,30). If the molecular mass isaccurate to 100 ppm, there are 7 possible combinations of dG+dC possiblefor each strand. However, if the measured molecular mass is accurate to10 ppm, there are only 2 combinations of dG+dC, and at 1 ppm accuracythere is only one possible base composition for each strand.

Signals from the mass spectrometer may be input to a maximum-likelihooddetection and classification algorithm such as is widely used in radarsignal processing. The detection processing uses matched filtering ofBCS observed in mass-basecount space and allows for detection andsubtraction of signatures from known, harmless organisms, and fordetection of unknown bioagent threats. Comparison of newly observedbioagents to known bioagents is also possible, for estimation of threatlevel, by comparing their BCS to those of known organisms and to knownforms of pathogenicity enhancement, such as insertion of antibioticresistance genes or toxin genes.

Processing may end with a Bayesian classifier using log likelihoodratios developed from the observed signals and average backgroundlevels. The program emphasizes performance predictions culminating inprobability-of-detection versus probability-of-false-alarm plots forconditions involving complex backgrounds of naturally occurringorganisms and environmental contaminants. Matched filters consist of apriori expectations of signal values given the set of primers used foreach of the bioagents. A genomic sequence database (e.g. GenBank) isused to define the mass basecount matched filters. The database containsknown threat agents and benign background organisms. The latter is usedto estimate and subtract the signature produced by the backgroundorganisms. A maximum likelihood detection of known background organismsis implemented using matched filters and a running-sum estimate of thenoise covariance. Background signal strengths are estimated and usedalong with the matched filters to form signatures that are thensubtracted. The maximum likelihood process is applied to this “cleanedup” data in a similar manner employing matched filters for the organismsand a running-sum estimate of the noise-covariance for the cleaned updata.

Although the molecular mass of amplification products obtained usingintelligent primers provides a means for identification of bioagents,conversion of molecular mass data to a base composition signature isuseful for certain analyses. As used herein, a “base compositionsignature” (BCS) is the exact base composition determined from themolecular mass of a bioagent identifying amplicon. In one embodiment, aBCS provides an index of a specific gene in a specific organism.

Base compositions, like sequences, vary slightly from isolate to isolatewithin species. It is possible to manage this diversity by building“base composition probability clouds” around the composition constraintsfor each species. This permits identification of organisms in a fashionsimilar to sequence analysis. A “pseudo four-dimensional plot” can beused to visualize the concept of base composition probability clouds(FIG. 18). Optimal primer design requires optimal choice of bioagentidentifying amplicons and maximizes the separation between the basecomposition signatures of individual bioagents. Areas where cloudsoverlap indicate regions that may result in a misclassification, aproblem which is overcome by selecting primers that provide informationfrom different bioagent identifying amplicons, ideally maximizing theseparation of base compositions. Thus, one aspect of the utility of ananalysis of base composition probability clouds is that it provides ameans for screening primer sets in order to avoid potentialmisclassifications of BCS and bioagent identity. Another aspect of theutility of base composition probability clouds is that they provide ameans for predicting the identity of a bioagent whose exact measured BCSwas not previously observed and/or indexed in a BCS database due toevolutionary transitions in its nucleic acid sequence.

It is important to note that, in contrast to probe-based techniques,mass spectrometry determination of base composition does not requireprior knowledge of the composition in order to make the measurement,only to interpret the results. In this regard, the present inventionprovides bioagent classifying information similar to DNA sequencing andphylogenetic analysis at a level sufficient to detect and identify agiven bioagent. Furthermore, the process of determination of apreviously unknown BCS for a given bioagent (for example, in a casewhere sequence information is unavailable) has downstream utility byproviding additional bioagent indexing information with which topopulate BCS databases. The process of future bioagent identification isthus greatly improved as more BCS indexes become available in the BCSdatabases.

Another embodiment of the present invention is a method of surveyingbioagent samples that enables detection and identification of allbacteria for which sequence information is available using a set oftwelve broad-range intelligent PCR primers. Six of the twelve primersare “broad range survey primers” herein defined as primers targeted tobroad divisions of bacteria (for example, the Bacillus/Clostridia groupor gamma-proteobacteria). The other six primers of the group of twelveprimers are “division-wide” primers herein defined as primers thatprovide more focused coverage and higher resolution. This method enablesidentification of nearly 100% of known bacteria at the species level. Afurther example of this embodiment of the present invention is a methodherein designated “survey/drill-down” wherein a subspeciescharacteristic for detected bioagents is obtained using additionalprimers. Examples of such a subspecies characteristic include but arenot limited to: antibiotic resistance, pathogenicity island, virulencefactor, strain type, sub-species type, and clade group. Using thesurvey/drill-down method, bioagent detection, confirmation and asubspecies characteristic can be provided within hours. Moreover, thesurvey/drill-down method can be focused to identify bioengineeringevents such as the insertion of a toxin gene into a bacterial speciesthat does not normally make the toxin.

The present methods allow extremely rapid and accurate detection andidentification of bioagents compared to existing methods. Furthermore,this rapid detection and identification is possible even when samplematerial is impure. The methods leverage ongoing biomedical research invirulence, pathogenicity, drug resistance and genome sequencing into amethod which provides greatly improved sensitivity, specificity andreliability compared to existing methods, with lower rates of falsepositives. Thus, the methods are useful in a wide variety of fields,including, but not limited to, those fields discussed below.

In other embodiments of the invention, the methods disclosed herein canidentify infectious agents in biological samples. At least a firstbiological sample containing at least a first unidentified infectiousagent is obtained. An identification analysis is carried out on thesample, whereby the first infectious agent in the first biologicalsample is identified. More particularly, a method of identifying aninfectious agent in a biological entity is provided. An identificationanalysis is carried out on a first biological sample obtained from thebiological entity, whereby at least one infectious agent in thebiological sample from the biological entity is identified. Theobtaining and the performing steps are, optionally, repeated on at leastone additional biological sample from the biological entity.

The present invention also provides methods of identifying an infectiousagent that is potentially the cause of a health condition in abiological entity. An identification analysis is carried out on a firsttest sample from a first infectious agent differentiating area of thebiological entity, whereby at least one infectious agent is identified.The obtaining and the performing steps are, optionally, repeated on anadditional infectious agent differentiating area of the biologicalentity.

Biological samples include, but are not limited to, hair, mucosa, skin,nail, blood, saliva, rectal, lung, stool, urine, breath, nasal, ocularsample, or the like. In some embodiments, one or more biological samplesare analyzed by the methods described herein. The biological sample(s)contain at least a first unidentified infectious agent and may containmore than one infectious agent. The biological sample(s) are obtainedfrom a biological entity. The biological sample can be obtained by avariety of manners such as by biopsy, swabbing, and the like. Thebiological samples may be obtained by a physician in a hospital or otherhealth care environment. The physician may then perform theidentification analysis or send the biological sample to a laboratory tocarry out the analysis.

Biological entities include, but are not limited to, a mammal, a bird,or a reptile. The biological entity may be a cow, horse, dog, cat, or aprimate. The biological entity can also be a human. The biologicalentity may be living or dead.

An infectious agent differentiating area is any area or location withina biological entity that can distinguish between a harmful versus normalhealth condition. An infectious agent differentiating area can be aregion or area of the biological entity whereby an infectious agent ismore likely to predominate from another region or area of the biologicalentity. For example, infectious agent differentiating areas may includethe blood vessels of the heart (heart disease, coronary artery disease,etc.), particular portions of the digestive system (ulcers, Crohn'sdisease, etc.), liver (hepatitis infections), and the like. In someembodiments, one or more biological samples from a plurality ofinfectious agent differentiating areas is analyzed the methods describedherein.

Infectious agents of the invention may potentially cause a healthcondition in a biological entity. Health conditions include anycondition, syndrome, illness, disease, or the like, identified currentlyor in the future by medical personnel. Infectious agents include, butare not limited to, bacteria, viruses, parasites, fungi, and the like.

In other embodiments of the invention, the methods disclosed herein canbe used to screen blood and other bodily fluids and tissues forpathogenic and non-pathogenic bacteria, viruses, parasites, fungi andthe like. Animal samples, including but not limited to, blood and otherbodily fluid and tissue samples, can be obtained from living animals,who are either known or not known to or suspected of having a disease,infection, or condition. Alternately, animal samples such as blood andother bodily fluid and tissue samples can be obtained from deceasedanimals. Blood samples can be further separated into plasma or cellularfractions and further screened as desired. Bodily fluids and tissues canbe obtained from any part of the animal or human body. Animal samplescan be obtained from, for example, mammals and humans.

Clinical samples are analyzed for disease causing bioagents andbiowarfare pathogens simultaneously with detection of bioagents atlevels as low as 100-1000 genomic copies in complex backgrounds withthroughput of approximately 100-300 samples with simultaneous detectionof bacteria and viruses. Such analyses provide additional value inprobing bioagent genomes for unanticipated modifications. These analysesare carried out in reference labs, hospitals and the LRN laboratories ofthe public health system in a coordinated fashion, with the ability toreport the results via a computer network to a common data-monitoringcenter in real time. Clonal propagation of specific infectious agents,as occurs in the epidemic outbreak of infectious disease, can be trackedwith base composition signatures, analogous to the pulse field gelelectrophoresis fingerprinting patterns used in tracking the spread ofspecific food pathogens in the Pulse Net system of the CDC (Swaminathanet al., Emerging Infectious Diseases, 2001, 7, 382-389). The presentinvention provides a digital barcode in the form of a series of basecomposition signatures, the combination of which is unique for eachknown organism. This capability enables real-time infectious diseasemonitoring across broad geographic locations, which may be essential ina simultaneous outbreak or attack in different cities.

In other embodiments of the invention, the methods disclosed herein canbe used for detecting the presence of pathogenic and non-pathogenicbacteria, viruses, parasites, fungi and the like in organ donors and/orin organs from donors. Such examination can result in the prevention ofthe transfer of, for example, viruses such as West Nile virus, hepatitisviruses, human immunodeficiency virus, and the like from a donor to arecipient via a transplanted organ. The methods disclosed herein canalso be used for detection of host versus graft or graft versus hostrejection issues related to organ donors by detecting the presence ofparticular antigens in either the graft or host known or suspected ofcausing such rejection. In particular, the bioagents in this regard arethe antigens of the major histocompatibility complex, such as the HLAantigens. The present methods can also be used to detect and trackemerging infectious diseases, such as West Nile virus infection,HIV-related diseases.

In other embodiments of the invention, the methods disclosed herein canbe used for pharmacogenetic analysis and medical diagnosis including,but not limited to, cancer diagnosis based on mutations andpolymorphisms, drug resistance and susceptibility testing, screening forand/or diagnosis of genetic diseases and conditions, and diagnosis ofinfectious diseases and conditions. In context of the present invention,pharmacogenetics is defined as the study of variability in drug responsedue to genetic factors. Pharmacogenetic investigations are often basedon correlating patient outcome with variations in genes involved in themode of action of a given drug. For example, receptor genes, or genesinvolved in metabolic pathways. The methods of the present inventionprovide a means to analyze the DNA of a patient to provide the basis forpharmacogenetic analysis.

The present method can also be used to detect single nucleotidepolymorphisms (SNPs), or multiple nucleotide polymorphisms, rapidly andaccurately. A SNP is defined as a single base pair site in the genomethat is different from one individual to another. The difference can beexpressed either as a deletion, an insertion or a substitution, and isfrequently linked to a disease state. Because they occur every 100-1000base pairs, SNPs are the most frequently bound type of genetic marker inthe human genome.

For example, sickle cell anemia results from an A-T transition, whichencodes a valine rather than a glutamic acid residue. Oligonucleotideprimers may be designed such that they bind to sequences that flank aSNP site, followed by nucleotide amplification and mass determination ofthe amplified product. Because the molecular masses of the resultingproduct from an individual who does not have sickle cell anemia isdifferent from that of the product from an individual who has thedisease, the method can be used to distinguish the two individuals.Thus, the method can be used to detect any known SNP in an individualand thus diagnose or determine increased susceptibility to a disease orcondition.

In one embodiment, blood is drawn from an individual and peripheralblood mononuclear cells (PBMC) are isolated and simultaneously tested,such as in a high-throughput screening method, for one or more SNPsusing appropriate primers based on the known sequences which flank theSNP region. The National Center for Biotechnology Information maintainsa publicly available database of SNPs on the world wide web of theInternet at, for example, “ncbi.nlm.nih.gov/SNP/.”

The method of the present invention can also be used for blood typing.The gene encoding A, B or U blood type can differ by four singlenucleotide polymorphisms. If the gene contains the sequenceCGTGGTGACCCTT (SEQ ID NO:5), antigen A results. If the gene contains thesequence CGTCGTCACCGCTA (SEQ ID NO:6) antigen B results. If the genecontains the sequence CGTGGT-ACCCCTT (SEQ ID NO:7), blood group 0results (“−” indicates a deletion). These sequences can be distinguishedby designing a single primer pair which flanks these regions, followedby amplification and mass determination.

The method of the present invention can also be used for detection andidentification of blood-borne pathogens such as Staphylococcus aureusfor example. The method of the present invention can also be used forstrain typing of respiratory pathogens in epidemic surveillance. Group Astreptococci (GAS), or Streptococcus pyogenes, is one of the mostconsequential causes of respiratory infections because of prevalence andability to cause disease with complications such as acute rheumaticfever and acute glomerulonephritis. GAS also causes infections of theskin (impetigo) and, in rare cases, invasive disease such as necrotizingfasciitis and toxic shock syndrome. Despite many decades of study, theunderlying microbial ecology and natural selection that favors enhancedvirulence and explosive GAS outbreaks is still poorly understood. Theability to detect GAS and multiple other pathogenic and non-pathogenicbacteria and viruses in patient samples would greatly facilitate ourunderstanding of GAS epidemics. It is also essential to be able tofollow the spread of virulent strains of GAS in populations and todistinguish virulent strains from less virulent or avirulentstreptococci that colonize the nose and throat of asymptomaticindividuals at a frequency ranging from 5-20% of the population (Bisno,A. L. (1995) in Principles and Practice of Infectious Diseases, eds.Mandell, G. L., Bennett, J. E. & Dolin, R. (Churchill Livingston, N.Y.),Vol. 2, pp. 1786-1799). Molecular methods have been developed to typeGAS based upon the sequence of the emm gene that encodes the M-proteinvirulence factor (Beall et al., J. Clin. Micro., 1996, 34, 953-958;Beall et al., J. Clin. Micro., 1997, 35, 1231-1235; and Facklam et al.,Emerging Infectious Diseases, 1999, 5, 247-253). Using this molecularclassification, over 150 different emm-types are defined and correlatedwith phenotypic properties of thousands of GAS isolates(www.cdc.gov/ncidod/biotech/strep/strepindex.html) (Facklam et al.,Clinical Infectious Diseases, 2002, 34, 28-38). Recently, a strategyknown as Multi Locus Sequence Typing (MLST) was developed to follow themolecular Epidemiology of GAS. In MLST, internal fragments of sevenhousekeeping genes are amplified, sequenced, and compared to a databaseof previously studied isolates (www.test.mlst.net/).

The present invention enables an emm-typing process to be carried outdirectly from throat swabs for a large number of samples within 12hours, allowing strain tracking of an ongoing epidemic, even ifgeographically dispersed, on a larger scale than ever before achievable.

In another embodiment, the present invention can be employed in theserotyping of viruses including, but not limited to, adenoviruses.Adenoviruses are DNA viruses that cause over 50% of febrile respiratoryillnesses in military recruits. Human adenoviruses are divided into sixmajor serogroups (A through F), each containing multiple strain types.Despite the prevalence of adenoviruses, there are no rapid methods fordetecting and serotyping adenoviruses.

In another embodiment, the present invention can be employed indistinguishing between members of the Orthopoxvirus genus. Smallpox iscaused by the Variola virus. Other members of the genus includeVaccinia, Monkeypox, Camelpox, and Cowpox. All are capable of infectinghumans, thus, a method capable of identifying and distinguishing amongmembers of the Orthopox genus is a worthwhile objective.

In another embodiment, the present invention can be employed indistinguishing between viral agents of viral hemorrhagic fevers (VHF).VHF agents include, but are not limited to, Filoviridae (Marburg virusand Ebola virus), Arenaviridae (Lassa, Junin, Machupo, Sabia, andGuanarito viruses), Bunyaviridae (Crimean-Congo hemorrhagic fever virus(CCHFV), Rift Valley fever virus, and Hanta viruses), and Flaviviridae(yellow fever virus and dengue virus). Infections by VHF viruses areassociated with a wide spectrum of clinical manifestations such asdiarrhea, myalgia, cough, headache, pneumonia, encephalopathy, andhepatitis. Filoviruses, arenaviruses, and CCHFV are of particularrelevance because they can be transmitted from human to human, thuscausing epidemics with high mortality rates (Khan et al., Am. J. Trop.Med. Hyg., 1997, 57, 519-525). In the absence of bleeding or organmanifestation, VHF is clinically difficult to diagnose, and the variousetiologic agents can hardly be distinguished by clinical tests. Currentapproaches to PCR detection of these agents are time-consuming, as theyinclude a separate cDNA synthesis step prior to PCR, agarose gelanalysis of PCR products, and in some instances a second round of nestedamplification or Southern hybridization. PCRs for different pathogenshave to be run assay by assay due to differences in cycling conditions,which complicate broad-range testing in a short period. Moreover,post-PCR processing or nested PCR steps included in currently usedassays increase the risk of false positive results due to carryovercontamination (Kwok et al., Nature, 1989, 339, 237-238).

In another embodiment, the present invention, can be employed in thediagnosis of a plurality of etiologic agents of a disease. An “etiologicagent” is herein defined as a pathogen acting as the causative agent ofa disease. Diseases may be caused by a plurality of etiologic agents.For example, recent studies have implicated both human herpesvirus 6(HHV-6) and the obligate intracellular bacterium Chlamydia pneumoniae inthe etiology of multiple sclerosis (Swanborg, Microbes and Infection,2002, 4, 1327-1333). The present invention can be applied to theidentification of multiple etiologic agents of a disease by, forexample, the use of broad range bacterial intelligent primers anddivision-wide primers (if necessary) for the identification of bacteriasuch as Chlamydia pneumoniae followed by primers directed to viralhousekeeping genes for the identification of viruses such as HHV-6, forexample.

In other embodiments of the invention, the methods disclosed herein canbe used for detection and identification of pathogens in livestock.Livestock includes, but is not limited to, cows, pigs, sheep, chickens,turkeys, goats, horses and other farm animals. For example, conditionsclassified by the California Department of Food and Agriculture asemergency conditions in livestock(www.cdfa.ca.gov/ahfss/ah/pdfs/CA_reportable_disease_list_(—)05292002.pdf)include, but are not limited to: Anthrax (Bacillus anthracis), Screwwormmyiasis (Cochliomyia hominivorax or Chrysomya bezziana), Africantrypanosomiasis (Tsetse fly diseases), Bovine babesiosis(piroplasmosis), Bovine spongiform encephalopathy (Mad Cow), Contagiousbovine pleuropneumonia (Mycoplasma mycoides mycoides small colony),Foot-and-mouth disease (Hoof-and-mouth), Heartwater (Cowdriaruminantium), Hemorrhagic septicemia (Pasteurella multocida serotypesB:2 or E:2), Lumpy skin disease, Malignant catarrhal fever (Africantype), Rift Valley fever, Rinderpest (Cattle plague), Theileriosis(Corridor disease, East Coast fever), Vesicular stomatitis, Contagiousagalactia (Mycoplasma species), Contagious caprine pleuropneumonia(Mycoplasma capricolum capripneumoniae), Nairobi sheep disease, Pestedesi petits ruminants (Goat plague), Pulmonary adenomatosis (Viralneoplastic pneumonia), Salmonella abortus ovis, Sheep and goat pox,African swine fever, Classical swine fever (Hog cholera), Japaneseencephalitis, Nipah virus, Swine vesicular disease, Teschen disease(Enterovirus encephalomyelitis), Vesicular exanthema, Exotic Newcastledisease (Viscerotropic velogenic Newcastle disease), Highly pathogenicavian influenza (Fowl plague), African horse sickness, Dourine(Trypanosoma equiperdum), Epizootic lymphangitis (equine blastomycosis,equine histoplasmosis), Equine piroplasmosis (Babesia equi, B. caballi),Glanders (Farcy) (Pseudomonas mallei), Hendra virus (Equinemorbillivirus), Horse pox, Surra (Trypanosoma evansi), Venezuelan equineencephalomyelitis, West Nile Virus, Chronic wasting disease in cervids,and Viral hemorrhagic disease of rabbits (calicivirus)

Conditions classified by the California Department of Food andAgriculture as regulated conditions in livestock include, but are notlimited to: rabies, Bovine brucellosis (Brucella abortus), Bovinetuberculosis (Mycobacterium bovis), Cattle scabies (multiple types),Trichomonosis (Tritrichomonas fetus), Caprine and ovine brucellosis(excluding Brucella ovis), Scrapie, Sheep scabies (Body mange)(Psoroptes ovis), Porcine brucellosis (Brucella suis), Pseudorabies(Aujeszky's disease), Ornithosis (Psittacosis or avian chlamydiosis)(Chlamydia psittaci), Pullorum disease (Fowl typhoid) (Salmonellagallinarum and pullorum), Contagious equine metritis (Taylorellaequigenitalis), Equine encephalomyelitis (Eastern and Western equineencephalitis), Equine infectious anemia (Swamp fever), Duck viralenteritis (Duck plague), and Tuberculosis in cervids.

Additional conditions monitored by the California Department of Food andAgriculture include, but are not limited to: Avian tuberculosis(Mycobacterium avium), Echinococcosis/Hydatidosis (Echinococcusspecies), Leptospirosis, Anaplasmosis (Anaplasma marginale or A.centrale), Bluetongue, Bovine cysticercosis (Taenia saginata in humans),Bovine genital campylobacteriosis (Campylobacter fetus venerealis),Dermatophilosis (Streptothricosis, mycotic dermatitis) (Dermatophiluscongolensis), Enzootic bovine leukosis (Bovine leukemia virus),Infectious bovine rhinotracheitis (Bovine herpesvirus-1), Johne'sdisease (Paratuberculosis) (Mycobacterium avium paratuberculosis),Malignant catarrhal fever (North American), Q Fever (Coxiella burnetii),Caprine (contagious) arthritis/encephalitis, Enzootic abortion of ewes(Ovine chlamydiosis) (Chlamydia psittaci), Maedi-Visna (Ovineprogressive pneumonia), Atrophic rhinitis (Bordetella bronchiseptica,Pasteurella multocida), Porcine cysticercosis (Taenia solium in humans),Porcine reproductive and respiratory syndrome, Transmissiblegastroenteritis (coronavirus), Trichinellosis (Trichinella spiralis),Avian infectious bronchitis, Avian infectious laryngotracheitis, Duckviral hepatitis, Fowl cholera (Pasteurella multocida), Fowl pox,Infectious bursal disease (Gumboro disease), Low pathogenic avianinfluenza, Marek's disease, Mycoplasmosis (Mycoplasma gallisepticum),Equine influenza Equine rhinopneumonitis (Equine herpesvirus-1), Equineviral arteritis, and Horse mange (multiple types).

A key problem in determining that an infectious outbreak is the resultof a bioterrorist attack is the sheer variety of organisms that might beused by terrorists. According to a recent review (Taylor et al., Philos.Trans. R. Soc. Lond. B. Biol. Sci., 2001, 356, 983-989), there are over1400 organisms infectious to humans; most of these have the potential tobe used in a deliberate, malicious attack. These numbers do not includenumerous strain variants of each organism, bioengineered versions, orpathogens that infect plants or animals. Paradoxically, most of the newtechnology being developed for detection of biological weaponsincorporates a version of quantitative PCR, which is based upon the useof highly specific primers and probes designed to selectively identifyspecific pathogenic organisms. This approach requires assumptions aboutthe type and strain of bacteria or virus which is expected to bedetected. Although this approach will work for the most obviousorganisms, like smallpox and anthrax, experience has shown that it isvery difficult to anticipate what a terrorist will do.

The present invention can be used to detect and identify any biologicalagent, including bacteria, viruses, fingi and toxins without priorknowledge of the organism being detected and identified. As one example,where the agent is a biological threat, the information obtained such asthe presence of toxin genes, pathogenicity islands and antibioticresistance genes for example, is used to determine practical informationneeded for countermeasures. In addition, the methods can be used toidentify natural or deliberate engineering events including chromosomefragment swapping, molecular breeding (gene shuffling) and emerginginfectious diseases. The present invention provides broad-functiontechnology that may be the only practical means for rapid diagnosis ofdisease caused by a biowarfare or bioterrorist attack, especially anattack that might otherwise be missed or mistaken for a more commoninfection.

Bacterial biological warfare agents capable of being detected by thepresent methods include, but are not limited to, Bacillus anthracis(anthrax), Yersinia pestis (pneumonic plague), Franciscella tularensis(tularemia), Brucella suis, Brucella abortus, Brucella melitensis(undulant fever), Burkholderia mallei (glanders), Burkholderiapseudomalleii (melioidosis), Salmonella typhi (typhoid fever),Rickettsia typhii (epidemic typhus), Rickettsia prowasekii (endemictyphus) and Coxiella burnetii (Q fever), Rhodobacter capsulatus,Chlamydia pneumoniae, Escherichia coli, Shigella dysenteriae, Shigellaflexneri, Bacillus cereus, Clostridium botulinum, Coxiella burnetti,Pseudomonas aeruginosa, Legionella pneumophila, and Vibrio cholerae.

Besides 16S and 23S rRNA, other target regions suitable for use in thepresent invention for detection of bacteria include, but are not limitedto, 5S rRNA and RNase P (FIG. 3).

Fungal biowarfare agents include, but are not limited to, Coccidioidesimmitis (Coccidioidomycosis), and Magnaporthe grisea.

Biological warfare toxin genes capable of being detected by the methodsof the present invention include, but are not limited to, botulinumtoxin, T-2 mycotoxins, ricin, staph enterotoxin B, shigatoxin, abrin,aflatoxin, Clostridium perfringens epsilon toxin, conotoxins,diacetoxyscirpenol, tetrodotoxin and saxitoxin.

Parasites that could be used in biological warfare include, but are notlimited to: Ascaris suum, Giardia lamblia, Cryptosporidium, andSchistosoma.

Biological warfare viral threat agents are mostly RNA viruses(positive-strand and negative-strand), with the exception of smallpox.Every RNA virus is a family of related viruses (quasispecies). Theseviruses mutate rapidly and the potential for engineered strains (naturalor deliberate) is very high. RNA viruses cluster into families that haveconserved RNA structural domains on the viral genome (e.g., virioncomponents, accessory proteins) and conserved housekeeping genes thatencode core viral proteins including, for single strand positive strandRNA viruses, RNA-dependent RNA polymerase, double stranded RNA helicase,chymotrypsin-like and papain-like proteases and methyltransferases.“Housekeeping genes” refers to genes that are generally always expressedand thought to be involved in routine cellular metabolism.

Examples of (−)-strand RNA viruses include, but are not limited to,arenaviruses (e.g., sabia virus, lassa fever, Machupo, Argentinehemorrhagic fever, flexal virus), bunyaviruses (e.g., hantavirus,nairovirus, phlebovirus, hantaan virus, Congo-crimean hemorrhagic fever,rift valley fever), and mononegavirales (e.g., filovirus, paramyxovirus,ebola virus, Marburg, equine morbillivirus).

Examples of (+)-strand RNA viruses include, but are not limited to,picornaviruses (e.g., coxsackievirus, echovirus, human coxsackievirus A,human echovirus, human enterovirus, human poliovirus, hepatitis A virus,human parechovirus, human rhinovirus), astroviruses (e.g., humanastrovirus), calciviruses (e.g., chiba virus, chitta virus, humancalcivirus, norwalk virus), nidovirales (e.g., human coronavirus, humantorovirus), flaviviruses (e.g., dengue virus 1-4, Japanese encephalitisvirus, Kyanasur forest disease virus, Murray Valley encephalitis virus,Rocio virus, St. Louis encephalitis virus, West Nile virus, yellow fevervirus, hepatitis c virus) and togaviruses (e.g., Chikugunya virus,Eastern equine encephalitis virus, Mayaro virus, O'nyong-nyong virus,Ross River virus, Venezuelan equine encephalitis virus, Rubella virus,hepatitis E virus). The hepatitis C virus has a 5′-untranslated regionof 340 nucleotides, an open reading frame encoding 9 proteins having3010 amino acids and a 3′-untranslated region of 240 nucleotides. The5′-UTR and 3′-UTR are 99% conserved in hepatitis C viruses.

In one embodiment, the target gene is an RNA-dependent RNA polymerase ora helicase encoded by (+)-strand RNA viruses, or RNA polymerase from a(−)-strand RNA virus. (+)-strand RNA viruses are double stranded RNA andreplicate by RNA-directed RNA synthesis using RNA-dependent RNApolymerase and the positive strand as a template. Helicase unwinds theRNA duplex to allow replication of the single stranded RNA. Theseviruses include viruses from the family picornaviridae (e.g.,poliovirus, coxsackievirus, echovirus), togaviridae (e.g., alphavirus,flavivirus, rubivirus), arenaviridae (e.g., lymphocytic choriomeningitisvirus, lassa fever virus), cononaviridae (e.g., human respiratory virus)and Hepatitis A virus. The genes encoding these proteins comprisevariable and highly conserved regions that flank the variable regions.

In one embodiment, the method can be used to detect the presence ofantibiotic resistance and/or toxin genes in a bacterial species. Forexample, Bacillus anthracis comprising a tetracycline resistance plasmidand plasmids encoding one or both anthracis toxins (px01 and/or px02)can be detected by using antibiotic resistance primer sets and toxingene primer sets. If the B. anthracis is positive for tetracyclineresistance, then a different antibiotic, for example quinalone, is used.

While the present invention has been described with specificity inaccordance with certain of its embodiments, the following examples serveonly to illustrate the invention and are not intended to limit the same.

EXAMPLES Example 1 Nucleic Acid Isolation and PCR

In one embodiment, nucleic acid is isolated from the organisms andamplified by PCR using standard methods prior to BCS determination bymass spectrometry. Nucleic acid is isolated, for example, by detergentlysis of bacterial cells, centrifugation and ethanol precipitation.Nucleic acid isolation methods are described in, for example, CurrentProtocols in Molecular Biology (Ausubel et al.) and Molecular Cloning; ALaboratory Manual (Sambrook et al.). The nucleic acid is then amplifiedusing standard methodology, such as PCR, with primers which bind toconserved regions of the nucleic acid which contain an interveningvariable sequence as described below.

General Genomic DNA Sample Prep Protocol: Raw samples are filtered usingSupor-200 0.2 μm membrane syringe filters (VWR International). Samplesare transferred to 1.5 ml eppendorf tubes pre-filled with 0.45 g of 0.7mm Zirconia beads followed by the addition of 350 μl of ATL buffer(Qiagen, Valencia, Calif.). The samples are subjected to bead beatingfor 10 minutes at a frequency of 19 l/s in a Retsch Vibration Mill(Retsch). After centrifugation, samples are transferred to an S-blockplate (Qiagen) and DNA isolation is completed with a BioRobot 8000nucleic acid isolation robot (Qiagen).

Swab Sample Protocol: Allegiance S/P brand culture swabs andcollection/transport system are used to collect samples. After drying,swabs are placed in 17×100 mm culture tubes (VWR International) and thegenomic nucleic acid isolation is carried out automatically with aQiagen Mdx robot and the Qiagen QIAamp DNA Blood BioRobot Mdx genomicpreparation kit (Qiagen, Valencia, Calif.).

Example 2 Mass Spectrometry

FTICR Instrumentation: The FTICR instrument is based on a 7 teslaactively shielded superconducting magnet and modified Bruker DaltonicsApex II 70e ion optics and vacuum chamber. The spectrometer isinterfaced to a LEAP PAL autosampler and a custom fluidics controlsystem for high throughput screening applications. Samples are analyzeddirectly from 96-well or 384-well microtiter plates at a rate of about 1sample/minute. The Bruker data-acquisition platform is supplemented witha lab-built ancillary NT datastation which controls the autosampler andcontains an arbitrary waveform generator capable of generating complexrf-excite waveforms (frequency sweeps, filtered noise, stored waveforminverse Fourier transform (SWIFT), etc.) for sophisticated tandem MSexperiments. For oligonucleotides in the 20-30-mer regime typicalperformance characteristics include mass resolving power in excess of100,000 (FWHM), low ppm mass measurement errors, and an operable m/zrange between 50 and 5000 m/z.

Modified ESI Source: In sample-limited analyses, analyte solutions aredelivered at 150 nL/minute to a 30 mm i.d. fused-silica ESI emittermounted on a 3-D micromanipulator. The ESI ion optics consists of aheated metal capillary, an rf-only hexapole, a skimmer cone, and anauxiliary gate electrode. The 6.2 cm rf-only hexapole is comprised of 1mm diameter rods and is operated at a voltage of 380 Vpp at a frequencyof 5 MHz. A lab-built electro-mechanical shutter can be employed toprevent the electrospray plume from entering the inlet capillary unlesstriggered to the “open” position via a TTL pulse from the data station.When in the “closed” position, a stable electrospray plume is maintainedbetween the ESI emitter and the face of the shutter. The back face ofthe shutter arm contains an elastomeric seal that can be positioned toform a vacuum seal with the inlet capillary. When the seal is removed, a1 mm gap between the shutter blade and the capillary inlet allowsconstant pressure in the external ion reservoir regardless of whetherthe shutter is in the open or closed position. When the shutter istriggered, a “time slice” of ions is allowed to enter the inletcapillary and is subsequently accumulated in the external ion reservoir.The rapid response time of the ion shutter (<25 ms) providesreproducible, user defined intervals during which ions can be injectedinto and accumulated in the external ion reservoir.

Apparatus for Infrared Multiphoton Dissociation: A 25 watt CW CO₂ laseroperating at 10.6 μm has been interfaced to the spectrometer to enableinfrared multiphoton dissociation (IRMPD) for oligonucleotide sequencingand other tandem MS applications. An aluminum optical bench ispositioned approximately 1.5 m from the actively shieldedsuperconducting magnet such that the laser beam is aligned with thecentral axis of the magnet. Using standard IR-compatible mirrors andkinematic mirror mounts, the unfocused 3 mm laser beam is aligned totraverse directly through the 3.5 mm holes in the trapping electrodes ofthe FTICR trapped ion cell and longitudinally traverse the hexapoleregion of the external ion guide finally impinging on the skimmer cone.This scheme allows IRMPD to be conducted in an m/z selective manner inthe trapped ion cell (e.g. following a SWIFT isolation of the species ofinterest), or in a broadband mode in the high pressure region of theexternal ion reservoir where collisions with neutral molecules stabilizeIRMPD-generated metastable fragment ions resulting in increased fragmention yield and sequence coverage.

Example 3 Identification of Bioagents

Table 2 shows a small cross section of a database of calculatedmolecular masses for over 9 primer sets and approximately 30 organisms.The primer sets were derived from rRNA alignment. Examples of regionsfrom rRNA consensus alignments are shown in FIGS. 1A-1C. Lines witharrows are examples of regions to which intelligent primer pairs for PCRare designed. The primer pairs are >95% conserved in the bacterialsequence database (currently over 10,000 organisms). The interveningregions are variable in length and/or composition, thus providing thebase composition “signature” (BCS) for each organism. Primer pairs werechosen so the total length of the amplified region is less than about80-90 nucleotides. The label for each primer pair represents thestarting and ending base number of the amplified region on the consensusdiagram.

Included in the short bacterial database cross-section in Table 2 aremany well known pathogens/biowarfare agents (shown in bold/red typeface)such as Bacillus anthracis or Yersinia pestis as well as some of thebacterial organisms found commonly in the natural environment such asStreptomyces. Even closely related organisms can be distinguished fromeach other by the appropriate choice of primers. For instance, two lowG+C organisms, Bacillus anthracis and Staph aureus, can be distinguishedfrom each other by using the primer pair defined by 16S_(—)1337 or23S_(—)855 (ΔM of 4 Da).

TABLE 2 Cross Section Of A Database Of Calculated Molecular Masses¹Primer Regions Bug Name 16S_971 16S_1100 16S_1337 16S_1294 16S_122823S_1021 23S_855 23S_193 23S_115 Acinetobacter calcoaceticus 55619.155004 28446.7 35854.9 51295.4 30299 42654 39557.5 54999

55005 54388 28448 35238 51296 30295 42651 39560 56850 Bacillus cereus55622.1 54387.9 28447.6 35854.9 51296.4 30295 42651 39560.5 56850.3Bordetella bronchiseptica 56857.3 51300.4 28446.7 35857.9 51307.4 3029942653 39559.5 51920.5 Borrelia burgdorferi 56231.2 55621.1 28440.735852.9 51295.4 30297 42029.9 38941.4 52524.6

58098 55011 28448 35854 50683 Campylobacter jejuni 58088.5 54386.929061.8 35856.9 50674.3 30294 42032.9 39558.5 45732.5

55000 55007 29063 35855 50676 30295 42036 38941 56230

55006 53767 28445 35855 51291 30300 42656 39562 54999 Clostridiumdifficile 56855.3 54386.9 28444.7 35853.9 51296.4 30294 41417.8 39556.555612.2 Enterococcus faecalis 55620.1 54387.9 28447.6 35858.9 51296.430297 42652 39559.5 56849.3

55622 55009 28445 35857 51301 30301 42656 39562 54999

53769 54385 28445 35856 51298 Haemophilus influenzae 55620.1 5500628444.7 35855.9 51298.4 30298 42656 39560.5 55613.1 Klebsiellapneumoniae 55622.1 55008 28442.7 35856.9 51297.4 30300 42655 39562.555000

55618 55626 28446 35857 51303 Mycobacterium avium 54390.9 55631.129064.8 35858.9 51915.5 30298 42656 38942.4 56241.2 Mycobacterium leprae54389.9 55629.1 29064.8 35860.9 51917.5 30298 42656 39559.5 56240.2Mycobacterium tuberculosis 54390.9 55629.1 29064.8 35860.9 51301.4 3029942656 39560.5 56243.2 Mycoplasma genitalium 53143.7 45115.4 29061.835854.9 50671.3 30294 43264.1 39558.5 56842.4 Mycoplasma pneumoniae53143.7 45118.4 29061.8 35854.9 50673.3 30294 43264.1 39559.5 56843.4Neisseria gonorrhoeae 55627.1 54389.9 28445.7 35855.9 51302.4 3030042649 39561.5 55000

55623 55010 28443 35858 51301 30298 43272 39558 55619

58093 55621 28448 35853 50677 30293 42650 39559 53139

58094 55623 28448 35853 50679 30293 42648 39559 53755

55622 55005 28445 35857 51301 30301 42658

55623 55009 28444 35857 51301 Staphylococcus aureus 56854.3 54386.928443.7 35852.9 51294.4 30298 42655 39559.5 57466.4 Streptomyces 54389.959341.6 29063.8 35858.9 51300.4 39563.5 56864.3 Treponema pallidum56245.2 55631.1 28445.7 35851.9 51297.4 30299 42034.9 38939.4 57473.4

55625 55626 28443 35857 52536 29063 30303 35241 50675 Vibrioparahaemolyticus 54384.9 55626.1 28444.7 34620.7 50064.2

55620 55626 28443 35857 51299 ¹Molecular mass distribution of PCRamplified regions for a selection of organisms (rows) across variousprimer pairs (columns). Pathogens are shown in bold. Empty cellsindicate presently incomplete or missing data.

FIG. 6 shows the use of ESI-FT-ICR MS for measurement of exact mass. Thespectra from 46 mer PCR products originating at position 1337 of the 16SrRNA from S. aureus (upper) and B. anthracis (lower) are shown. Thesedata are from the region of the spectrum containing signals from the[M-8H+]⁸⁻ charge states of the respective 5′-3′ strands. The two strandsdiffer by two (AT→CG) substitutions, and have measured masses of14206.396 and 14208.373+0.010 Da, respectively. The possible basecompositions derived from the masses of the forward and reverse strandsfor the B. anthracis products are listed in Table 3.

TABLE 3 Possible base composition for B. anthracis products Calc. MassError Base Comp. 14208.2935 0.079520 A1 G17 C10 T18 14208.3160 0.056980A1 G20 C15 T10 14208.3386 0.034440 A1 G23 C20 T2 14208.3074 0.065560 A6G11 C3 T26 14208.3300 0.043020 A6 G14 C8 T18 14208.3525 0.020480 A6 G17C13 T10 14208.3751 0.002060 A6 G20 C18 T2 14208.3439 0.029060 A11 G8 C1T26 14208.3665 0.006520 A11 G11 C6 T18 14208.3890 0.016020 A11 G14 C11T10 14208.4116 0.038560 A11 G17 C16 T2 14208.4030 0.029980 A16 G8 C4 T1814208.4255 0.052520 A16 G11 C9 T10 14208.4481 0.075060 A16 G14 C14 T214208.4395 0.066480 A21 G5 C2 T18 14208.4620 0.089020 A21 G8 C7 T1014079.2624 0.080600 A0 G14 C13 T19 14079.2849 0.058060 A0 G17 C18 T1114079.3075 0.035520 A0 G20 C23 T3 14079.2538 0.089180 A5 G5 C1 T3514079.2764 0.066640 A5 G8 C6 T27 14079.2989 0.044100 A5 G11 C11 T1914079.3214 0.021560 A5 G14 C16 T11 14079.3440 0.000980 A5 G17 C21 T314079.3129 0.030140 A10 G5 C4 T27 14079.3354 0.007600 A10 G8 C9 T1914079.3579 0.014940 A10 G11 C14 T11 14079.3805 0.037480 A10 G14 C19 T314079.3494 0.006360 A15 G2 C2 T27 14079.3719 0.028900 A15 G5 C7 T1914079.3944 0.051440 A15 G8 C12 T11 14079.4170 0.073980 A15 G11 C17 T314079.4084 0.065400 A20 G2 C5 T19 14079.4309 0.087940 A20 G5 C10 T13Among the 16 compositions for the forward strand and the 18 compositionsfor the reverse strand that were calculated, only one pair (shown inbold) are complementary, corresponding to the actual base compositionsof the B. anthracis PCR products.

Example 4 BCS of Region from Bacillus anthracis and Bacillus cereus

A conserved Bacillus region from B. anthracis (A₁₄G₉C₁₄T₉) and B. cereus(A₁₅G₉C₁₃T₉) having a C to A base change was synthesized and subjectedto ESI-TOF MS. The results are shown in FIG. 7 in which the two regionsare clearly distinguished using the method of the present invention(MW=14072.26 vs. 14096.29).

Example 5 Identification of Additional Bioagents

In other examples of the present invention, the pathogen Vibrio choleracan be distinguished from Vibrio parahemolyticus with ΔM>600 Da usingone of three 16S primer sets shown in Table 2 (16S_(—)971, 16S_(—)1228or 16S_(—)1294) as shown in Table 4. The two mycoplasma species in thelist (M. genitalium and M. pneumoniae) can also be distinguished fromeach other, as can the three mycobacteriae. While the direct massmeasurements of amplified products can identify and distinguish a largenumber of organisms, measurement of the base composition signatureprovides dramatically enhanced resolving power for closely relatedorganisms. In cases such as Bacillus anthracis and Bacillus cereus thatare virtually indistinguishable from each other based solely on massdifferences, compositional analysis or fragmentation patterns are usedto resolve the differences. The single base difference between the twoorganisms yields different fragmentation patterns, and despite thepresence of the ambiguous/unidentified base N at position 20 in B.anthracis, the two organisms can be identified.

Tables 4a-b show examples of primer pairs from Table 1 which distinguishpathogens from background.

TABLE 4a Organism name 23S_855 16S_1337 23S_1021 Bacillus anthracis42650.98 28447.65 30294.98 Staphylococcus aureus 42654.97 28443.6730297.96

TABLE 4b Organism name 16S_971 16S_1294 16S_1228 Vibrio cholerae55625.09 35856.87 52535.59 Vibrio parahaemolyticus 54384.91 34620.6750064.19

Table 5 shows the expected molecular weight and base composition ofregion 16S_(—)1100-1188 in Mycobacterium avium and Streptomyces sp.

TABLE 5 Organism Molecular Region name Length weight Base comp.16S_1100-1188 Mycobac- 82 25624.1728 A₁₆G₃₂C₁₈T₁₆ terium avium16S_1100-1188 Strepto- 96 29904.871 A₁₇G₃₈C₂₇T₁₄ myces sp.

Table 6 shows base composition (single strand) results for16S_(—)1100-1188 primer amplification reactions different species ofbacteria. Species which are repeated in the table (e.g., Clostridiumbotulinum) are different strains which have different base compositionsin the 16S_(—)1100-1188 region.

TABLE 6 Organism name Base comp. Mycobacterium avium A₁₆G₃₂C₁₈T₁₆Streptomyces sp. A₁₇G₃₈C₂₇T₁₄ Ureaplasma urealyticum A₁₈G₃₀C₁₇T₁₇Streptomyces sp. A₁₉G₃₆C₂₄T₁₈ Mycobacterium leprae A₂₀G₃₂C₂₂T₁₆

A ₂₀ G ₃₃ C ₂₁ T ₁₆

A ₂₀ G ₃₃ C ₂₁ T ₁₆ Fusobacterium necroforum A₂₁G₂₆C₂₂T₁₈ Listeriamonocytogenes A₂₁G₂₇C₁₉T₁₉ Clostridium botulinum A₂₁G₂₇C₁₉T₂₁ Neisseriagonorrhoeae A₂₁G₂₈C₂₁T₁₈ Bartonella quintana A₂₁G₃₀C₂₂T₁₆ Enterococcusfaecalis A₂₂G₂₇C₂₀T₁₉ Bacillus megaterium A₂₂G₂₈C₂₀T₁₈ Bacillus subtilisA₂₂G₂₈C₂₁T₁₇ Pseudomonas aeruginosa A₂₂G₂₉C₂₃T₁₅ Legionella pneumophilaA₂₂G₃₂C₂₀T₁₆ Mycoplasma pneumoniae A₂₃G₂₀C₁₄T₁₆ Clostridium botulinumA₂₃G₂₆C₂₀T₁₉ Enterococcus faecium A₂₃G₂₆C₂₁T₁₈ Acinetobacter calcoacetiA₂₃G₂₆C₂₁T₁₉

A ₂₃ G ₂₆ C ₂₄ T ₁₅

A ₂₃ G ₂₆ C ₂₄ T ₁₅ Clostridium perfringens A₂₃G₂₇C₁₉T₁₉

A ₂₃ G ₂₇ C ₂₀ T ₁₈

A ₂₃ G ₂₇ C ₂₀ T ₁₈

A ₂₃ G ₂₇ C ₂₀ T ₁₈ Aeromonas hydrophila A ₂₃ G ₂₉ C ₂₁ T ₁₆ Escherichiacoli A ₂₃ G ₂₉ C ₂₁ T ₁₆ Pseudomonas putida A₂₃G₂₉C₂₁T₁₇

A ₂₃ G ₂₉ C ₂₂ T ₁₅

A ₂₃ G ₂₉ C ₂₂ T ₁₅ Vibrio cholerae A₂₃G₃₀C₂₁T₁₆

A ₂₃ G ₃₁ C ₂₁ T ₁₅

A ₂₃ G ₃₁ C ₂₁ T ₁₅ Mycoplasma genitalium A₂₄G₁₉C₁₂T₁₈ Clostridiumbotulinum A₂₄G₂₅C₁₈T₂₀ Bordetella bronchiseptica A₂₄G₂₆C₁₉T₁₄Francisella tularensis A₂₄G₂₆C₁₉T₁₉

A ₂₄ G ₂₆ C ₂₀ T ₁₈

A ₂₄ G ₂₆ C ₂₀ T ₁₈

A ₂₄ G ₂₆ C ₂₀ T ₁₈ Helicobacter pylori A₂₄G₂₆C₂₀T₁₉ Helicobacter pyloriA₂₄G₂₆C₂₁T₁₈ Moraxella catarrhalis A₂₄G₂₆C₂₃T₁₆ Haemophilus influenzaeRd A₂₄G₂₈C₂₀T₁₇

A ₂₄ G ₂₈ C ₂₁ T ₁₆

A ₂₄ G ₂₈ C ₂₁ T ₁₆

 AR39 A ₂₄ G ₂₈ C ₂₁ T ₁₆ Pseudomonas putida A₂₄G₂₉C₂₁T₁₆

A ₂₄ G ₃₀ C ₂₁ T ₁₅

A ₂₄ G ₃₀ C ₂₁ T ₁₅

A ₂₄ G ₃₀ C ₂₁ T ₁₅ Clostridium botulinum A₂₅G₂₄C₁₈T₂₁ Clostridiumtetani A₂₅G₂₅C₁₈T₂₀ Francisella tularensis A₂₅G₂₅C₁₉T₁₉ Acinetobactercalcoacetic A₂₅G₂₆C₂₀T₁₉ Bacteriodes fragilis A₂₅G₂₇C₁₆T₂₂ Chlamydophilapsittaci A₂₅G₂₇C₂₁T₁₆ Borrelia burgdorferi A₂₅G₂₉C₁₇T₁₉ Streptobacillusmonilifor A₂₆G₂₆C₂₀T₁₆ Rickettsia prowazekii A₂₆G₂₈C₁₈T₁₈ Rickettsiarickettsii A₂₆G₂₈C₂₀T₁₆ Mycoplasma mycoides A₂₈G₂₃C₁₆T₂₀

The same organism having different base compositions are differentstrains. Groups of organisms which are highlighted or in italics havethe same base compositions in the amplified region. Some of theseorganisms can be distinguished using multiple primers. For example,Bacillus anthracis can be distinguished from Bacillus cereus andBacillus thuringiensis using the primer 16S_(—)971-1062 (Table 7). Otherprimer pairs which produce unique base composition signatures are shownin Table 6 (bold). Clusters containing very similar threat andubiquitous non-threat organisms (e.g. anthracis cluster) aredistinguished at high resolution with focused sets of primer pairs. Theknown biowarfare agents in Table 6 are Bacillus anthracis, Yersiniapestis, Francisella tularensis and Rickettsia prowazekii.

TABLE 7 Organism 16S_971-1062 16S_1228-1310 16S_1100-1188 Aeromonashydrophila A₂₁G₂₉C₂₂T₂₀ A₂₂G₂₇C₂₁T₁₃ A₂₃G₃₁C₂₁T₁₅ Aeromonas salmonicidaA₂₁G₂₉C₂₂T₂₀ A₂₂G₂₇C₂₁T₁₃ A₂₃G₃₁C₂₁T₁₅ Bacillus anthracis A ₂₁ G ₂₇ C ₂₂T ₂₂ A₂₄G₂₂C₁₉T₁₈ A₂₃G₂₇C₂₀T₁₈ Bacillus cereus A₂₂G₂₇C₂₂T₂₂ A₂₄G₂₂C₁₉T₁₈A₂₃G₂₇C₂₀T₁₈ Bacillus thuringiensis A₂₂G₂₇C₂₁T₂₂ A₂₄G₂₂C₁₉T₁₈A₂₃G₂₇C₂₀T₁₈ Chlamydia trachomatis A ₂₂ G ₂₆ C ₂₀ T ₂₃ A ₂₄ G ₂₃ C ₁₉ T₁₆ A₂₄G₂₈C₂₁T₁₆ Chlamydia pneumoniae AR39 A₂₆G₂₃C₂₀T₂₂ A₂₆G₂₂C₁₆T₁₈A₂₄G₂₈C₂₁T₁₆ Leptospira borgpetersenii A₂₂G₂₆C₂₀T₂₁ A₂₂G₂₅C₂₁T₁₅A₂₃G₂₆C₂₄T₁₅ Leptospira interrogans A₂₂G₂₆C₂₀T₂₁ A₂₂G₂₅C₂₁T₁₅A₂₃G₂₆C₂₄T₁₅ Mycoplasma genitalium A₂₈G₂₃C₁₅T₂₂ A ₃₀ G ₁₈ C ₁₅ T ₁₉ A ₂₄G ₁₉ C ₁₂ T ₁₈ Mycoplasma pneumoniae A₂₈G₂₃C₁₅T₂₂ A ₂₇ G ₁₉ C ₁₆ T ₂₀ A₂₃ G ₂₀ C ₁₄ T ₁₆ Escherichia coli A ₂₂ G ₂₈ C ₂₀ T ₂₂ A₂₄G₂₅C₂₁T₁₃A₂₃G₂₉C₂₂T₁₅ Shigella dysenteriae A ₂₂ G ₂₈ C ₂₁ T ₂₁ A₂₄G₂₅C₂₁T₁₃A₂₃G₂₉C₂₂T₁₅ Proteus vulgaris A ₂₃ G ₂₆ C ₂₂ T ₂₁ A ₂₆ G ₂₄ C ₁₉ T ₁₄A₂₄G₃₀C₂₁T₁₅ Yersinia pestis A₂₄G₂₅C₂₁T₂₂ A₂₅G₂₄C₂₀T₁₄ A₂₄G₃₀C₂₁T₁₅Yersinia pseudotuberculosis A₂₄G₂₅C₂₁T₂₂ A₂₅G₂₄C₂₀T₁₄ A₂₄G₃₀C₂₁T₁₅Francisella tularensis A ₂₀ G ₂₅ C ₂₁ T ₂₃ A ₂₃ G ₂₆ C ₁₇ T ₁₇ A ₂₄ G ₂₆C ₁₉ T ₁₉ Rickettsia prowazekii A ₂₁ G ₂₆ C ₂₄ T ₂₅ A ₂₄ G ₂₃ C ₁₆ T ₁₉A ₂₆ G ₂₈ C ₁₈ T ₁₈ Rickettsia rickettsii A ₂₁ G ₂₆ C ₂₅ T ₂₄ A ₂₄ G ₂₄C ₁₇ T ₁₇ A ₂₆ G ₂₈ C ₂₀ T ₁₆

The sequence of B. anthracis and B. cereus in region 16S_(—)971 is shownbelow. Shown in bold is the single base difference between the twospecies that can be detected using the methods of the present invention.B. anthracis has an ambiguous base at position 20.

B.anthracis_16S_971 (SEQ ID NO:1)GCGAAGAACCUUACCAGGUNUUGACAUCCUCUGACAACCCUAGAGAUAGGGCUUCUCCUUCGGGAGCAGAGUGACAGGUGGUGCAUGGUU B.cereus_16S_971 (SEQ ID NO:2)GCGAAGAACCUUACCAGGUCUUGACAUCCUCUGAAAACCCUAGAGAUAGGGCUUCUCCUUCGGGAGCAGAGUGACAGGUGGUGCAUGGUU

Example 6 ESI-TOF MS of sspE 56-mer Plus Calibrant

The mass measurement accuracy that can be obtained using an internalmass standard in the ESI-MS study of PCR products is shown in FIG. 8.The mass standard was a 20-mer phosphorothioate oligonucleotide added toa solution containing a 56-mer PCR product from the B. anthracis sporecoat protein sspE. The mass of the expected PCR product distinguishes B.anthracis from other species of Bacillus such as B. thuringiensis and B.cereus.

Example 7 B. anthracis ESI-TOF Synthetic 16S_(—)1228 Duplex

An ESI-TOF MS spectrum was obtained from an aqueous solution containing5 μM each of synthetic analogs of the expected forward and reverse PCRproducts from the nucleotide 1228 region of the B. anthracis 16S rRNAgene. The results (FIG. 9) show that the molecular weights of theforward and reverse strands can be accurately determined and easilydistinguish the two strands. The [M-21H⁺]²¹⁻ and [M-20H⁺]²⁰ ⁻ chargestates are shown.

Example 8 ESI-FTICR-MS of Synthetic B. anthracis 16S_(—)1337 46 BasePair Duplex

An ESI-FTICR-MS spectrum was obtained from an aqueous solutioncontaining 5 μM each of synthetic analogs of the expected forward andreverse PCR products from the nucleotide 1337 region of the B. anthracis16S rRNA gene. The results (FIG. 10) show that the molecular weights ofthe strands can be distinguished by this method. The [M-16H⁺]¹⁶⁻ through[M-10H⁺]¹⁰⁻ charge states are shown. The insert highlights theresolution that can be realized on the FTICR-MS instrument, which allowsthe charge state of the ion to be determined from the mass differencebetween peaks differing by a single 13C substitution.

Example 9 ESI-TOF MS of 56-mer Oligonucleotide from saspB Gene of B.anthracis with Internal Mass Standard

ESI-TOF MS spectra were obtained on a synthetic 56-mer oligonucleotide(5 μM) from the saspB gene of B. anthracis containing an internal massstandard at an ESI of 1.7 μL/min as a function of sample consumption.The results (FIG. 11) show that the signal to noise is improved as morescans are summed, and that the standard and the product are visibleafter only 100 scans.

Example 10 ESI-TOF MS of an Internal Standard with Tributylammonium(TBA)-Trifluoroacetate (TFA) Buffer

An ESI-TOF-MS spectrum of a 20-mer phosphorothioate mass standard wasobtained following addition of 5 mM TBA-TFA buffer to the solution. Thisbuffer strips charge from the oligonucleotide and shifts the mostabundant charge state from [M-8H⁺]⁸⁻ to [M-3H+]³⁻ (FIG. 12).

Example 11 Master Database Comparison

The molecular masses obtained through Examples 1-10 are compared tomolecular masses of known bioagents stored in a master database toobtain a high probability matching molecular mass.

Example 12 Master Data Base Interrogation over the Internet

The same procedure as in Example 11 is followed except that the localcomputer did not store the Master database. The Master database isinterrogated over an internet connection, searching for a molecular massmatch.

Example 13 Master Database Updating

The same procedure as in example 11 is followed except the localcomputer is connected to the internet and has the ability to store amaster database locally. The local computer system periodically, or atthe user's discretion, interrogates the Master database, synchronizingthe local master database with the global Master database. This providesthe current molecular mass information to both the local database aswell as to the global Master database. This further provides more of aglobalized knowledge base.

Example 14 Global Database Updating

The same procedure as in example 13 is followed except there arenumerous such local stations throughout the world. The synchronizationof each database adds to the diversity of information and diversity ofthe molecular masses of known bioagents.

Example 15 Demonstration of Detection and Identification of Five Speciesof Bacteria in a Mixture

Broad range intelligent primers were chosen following analysis of alarge collection of curated bacterial 16S rRNA sequences representinggreater than 4000 species of bacteria. Examples of primers capable ofpriming from greater than 90% of the organisms in the collectioninclude, but are not limited to, those exhibited in Table 8 whereinTp=5′propynylated uridine and Cp=5′propynylated cytidine.

TABLE 8 Intelligent Primer Pairs for Identification of Bacteria ForwardReverse Primer Forward Primer SEQ ID Reverse Primer SEQ ID Pair NameSequence NO: Sequence NO: 16S_EC_1077_ GTGAGATGTTGGGTTAAGTCCC 8GACGTCATCCCCACCTTCCTC 9 1195 GTAACGAG 16S_EC_1082-ATGTTGGGTTAAGTCCCGCAAC 10 TTGACGTCATCCCCACCTTCCT 11 1197 GAG C16S_EC_1090_ TTAAGTCCCGCAACGATCGCAA 12 TGACGTCATCCCCACCTTCCTC 13 119616S_EC_1222_ GCTACACACGTGCTACAATG 14 CGAGTTGCAGACTGCGATCCG 15 132316S_EC_1332_ AAGTCGGAATCGCTAGTAATCG 16 GACGGGCGGTGTGTACAAG 17 140716S_EC_30_ TGAACGCTGGTGGCATGCTTAA 18 TACGCATTACTCACCCGTCCGC 19 126 CAC16S_EC_38_ GTGGCATGCCTAATACATGCAA 20 TTACTCACCCGTCCGCCGCT 21 120 GTCG16S_EC_49_ TAACACATGCAAGTCGAACG 22 TTACTCACCCGTCCGCC 23 120 16S_EC_683_GTGTAGCGGTGAAATGCG 24 GTATCTAATCCTGTTTGCTCCC 25 795 16S_EC_713_AGAACACCGATGGCGAAGGC 26 CGTGGACTACCAGGGTATCTA 27 809 16S_EC_785_GGATTAGAGACCCTGGTAGTCC 28 GGCCGTACTCCCCAGGCG 29 897 16S_EC_785_GGATTAGATACCCTGGTAGTCC 30 GGCCGTACTCCCCAGGCG 31 897_2 ACGC 16S_EC_789_TAGATACCCTGGTAGTCCACGC 32 CGTACTCCCCAGGCG 33 894 16S_EC_960_TTCGATGCAACGCGAAGAACCT 34 ACGAGCTGACGACAGCCATG 35 1073 16S_EC_969_ACGCGAAGAACCTTACC 36 ACGACACGAGCTGACGAC 37 1078 23S_EC_1826_CTGACACCTGCCCGGTGC 38 GACCGTTATAGTTACGGCC 39 1924 23S_EC_2645_TCTGTCCCTAGTACGAGAGGAC 40 TGCTTAGATGCTTTCAGC 41 2761 CGG 23S_EC_2645_CTGTCCCTAGTACGAGAGGACC 42 GTTTCATGCTTAGATGCTTTCA 43 2767 GG GC23S_EC_493_ GGGGAGTGAAAGAGATCCTGAA 44 ACAAAAGGTACGCCGTCACCC 45 571 ACCG23S_EC_493_ GGGGAGTGAAAGAGATCCTGAA 46 ACAAAAGGCACGCCATCACCC 47 571_2ACCG 23S_EC_971_ CGAGAGGGAAACAACCCAGACC 48 TGGCTGCTTCTAAGCCAAC 49 1077INFB_EC_1365_ TGCTCGTGGTGCACAAGTAACG 50 TGCTGCTTTCGCATGGTTAATT 51 1467GATATTA GCTTCAA RPOC_EC_1018_ CAAAACTTATTAGGTAAGCGTG 52TCAAGCGCCATTTCTTTTGGTA 53 1124 TTGACT AACCACAT RPOC_EC_1018_CAAAACTTATTAGGTAAGCGTG 54 TCAAGCGCCATCTCTTTCGGTA 55 1124_2 TTGACTATCCACAT RPOC_EC_114_ TAAGAAGCCGGAAACCATCAAC 56 GGCGCTTGTACTTACCGCAC 57232 TACCG RPOC_EC_2178_ TGATTCTGGTGCCCGTGGT 58 TTGGCCATCAGGCCACGCATAC 592246 RPOC_EC_2178_ TGATTCCGGTGCCCGTGGT 60 TTGGCCATCAGACCACGCATAC 612246_2 RPOC_EC_2218_ CTGGCAGGTATGCGTGGTCTGA 62 CGCACCGTGGGTTGAGATGAAG 632337 TG TAC RPOC_EC_2218_ CTTGCTGGTATGCGTGGTCTGA 64CGCACCATGCGTAGAGATGAAG 65 2337_2 TG TAC RPOC_EC_808_CGTCGGGTGATTAACCGTAACA 66 GTTTTTCGTTGCGTACGATGAT 67 889 ACCG GTCRPOC_EC_808_ CGTCGTGTAATTAACCGTAACA 68 ACGTTTTTCGTTTTGAACGATA 69 891ACCG ATGCT RPOC_EC_993_ CAAAGGTAAGCAAGGTCGTTTC 70 CGAACGGCCTGAGTAGTCAACA71 1059 CGTCA CG RPOC_EC_993_ CAAAGGTAAGCAAGGACGTTTC 72CGAACGGCCAGAGTAGTCAACA 73 1059_2 CGTCA CG TUFB_EC_239_TAGACTGCCCAGGACACGCTG 74 GCCGTCCATCTGAGCAGCACC 75 303 TUFB_EC_239_TTGACTGCCCAGGTCACGCTG 76 GCCGTCCATTTGAGCAGCACC 77 303_2 TUFB_EC_976_AACTACCGTCCGCAGTTCTACT 78 GTTGTCGCCAGGCATAACCATT 79 1068 TCC TCTUFB_EC_976_ AACTACCGTCCTCAGTTCTACT 80 GTTGTCACCAGGCATTACCATT 81 1068_2TCC TC TUFB_EC_985_ CCACAGTTCTACTTCCGTACTA 82 TCCAGGCATTACCATTTCTACT 831062 CTGACG CCTTCTGG RPLB_EC_650_ GACCTACAGTAAGAGGTTCTGT 84TCCAAGTGCTGGTTTACCCCAT 85 762 AATGAACC GG RPLB_EC_688_CATCCACACGGTGGTGGTGAAG 86 GTGCTGGTTTACCCCATGGAGT 87 757 G RPOC_EC_1036_CGTGTTGACTATTCGGGGCGTT 88 ATTCAAGAGCCATTTCTTTTGG 89 1126 CAG TAAACCACRPOB_EC_3762_ TCAACAACCTCTTGGAGGTAAA 90 TTTCTTGAAGAGTATGAGCTGC 91 3865GCTCAGT TCCGTAAG RPLB_EC_688_ CATCCACACGGTGGTGGTGAAG 92TGTTTTGTATCCAAGTGCTGGT 93 771 G TTACCCC VALS_EC_1105_CGTGGCGGCGTGGTTATCGA 94 CGGTACGAACTGGATGTCGCCG 95 1218 TT RPOB_EC_1845_TATCGCTCAGGCGAACTCCAAC 96 GCTGGATTCGCCTTTGCTACG 97 1929 RPLB_EC_669_TGTAATGAACCCTAATGACCAT 98 CCAAGTGCTGGTTTACCCCATG 99 761 CCACACGG GAGTARPLB_EC_671_ TAATGAACCCTAATGACCATCC 100 TCCAAGTGCTGGTTTACCCCAT 101 762ACACGGTG GGAG RPOB_EC_3775_ CTTGGAGGTAAGTCTCATTTTG 102CGTATAAGCTGCACCATAAGCT 103 3858 GTGGGCA TGTAATGC VALS_EC_1833_CGACGCGCTGCGCTTCAC 104 GCGTTCCACAGCTTGTTGCAGA 105 1943 AG RPOB_EC_1336_GACCACCTCGGCAACCGT 106 TTCGCTCTCGGCCTGGCC 107 1455 TUFB_EC_225_GCACTATGCACACGTAGATTGT 108 TATAGCACCATCCATCTGAGCG 109 309 CCTGG GCACDNAK_EC_428_ CGGCGTACTTCAACGACAGCCA 110 CGCGGTCGGCTCGTTGATGA 111 522VALS_EC_1920_ CTTCTGCAACAAGCTGTGGAAC 112 TCGCAGTTCATCAGCACGAAGC 113 1970GC G TUFB_EC_757_ AAGACGACCTGCACGGGC 114 GCGCTCCACGTCTTCACGC 115 86723S_EC_2646_ CTGTTCTTAGTACGAGAGGACC 116 TTCGTGCTTAGATGCTTTCAG 117 276516S_EC_969_ ACGCGAAGAACCTTACpC 118 ACGACACGAGCpTpGACGAC 119 1078_3P16S_EC_972_ CGAAGAACpCpTTACC 120 ACACGAGCpTpGAC 121 1075_4P 16S_EC_972_CGAAGAACCTTACC 122 ACACGAGCTGAC 123 1075 23S_EC_−347_CCTGATAAGGGTGAGGTCG 124 ACGTCCTTCATCGCCTCTGA 125 59 23S_EC_−7_GTTGTGAGGTTAAGCGACTAAG 126 CTATCGGTCAGTCAGGAGTAT 127 450 23S_EC_−7_GTTGTGAGGTTAAGCGACTAAG 128 TTGCATCGGGTTGGTAAGTC 129 910 23S_EC_430_ATACTCCTGACTGACCGATAG 130 AACATAGCCTTCTCCGTCC 131 1442 23S_EC_891_GACTTACCAACCCGATGCAA 132 TACCTTAGGACCGTTATAGTTA 133 1931 CG 23S_EC_1424_GGACGGAGAAGGCTATGTT 134 CCAAACACCGCCGTCGATAT 135 2494 23S_EC_1908_CGTAACTATAACGGTCCTAAGG 136 GCTTACACACCCGGCCTATC 137 2852 TA 23S_EC_2475_ATATCGACGGCGGTGTTTGG 138 GCGTGACAGGCAGCTATTC 139 3209 16S_EC_−60_AGTCTCAAGAGTGAACACGTAA 140 GCTGCTGGCACGGAGTTA 141 525 16S_EC_326_GACACGGTCCAGACTCCTAC 142 CCATGCAGCACCTGTCTC 143 1058 16S_EC_705_GATCTGGAGGAATACCGGTG 144 ACGGTTACCTTGTTACGACT 145 1512 16S_EC_1268_GAGAGCAAGCGGACCTCATA 146 CCTCCTGCGTGCAAAGC 147 1775 GROL_EC_941_TGGAAGATCTGGGTCAGGC 148 CAATCTGCTGACGGATCTGAGC 149 1060 INFB_EC_1103_GTCGTGAAAACGAGCTGGAAGA 150 CATGATGGTCACAACCGG 151 1191 HFLB_EC_1082_TGGCGAACCTGGTGAACGAAGC 152 CTTTCGCTTTCTCGAACTCAAC 153 1168 CATINFB_EC_1969_ CGTCAGGGTAAATTCCGTGAAG 154 AACTTCGCCTTCGGTCATGTT 155 2058TTAA GROL_EC_219_ GGTGAAAGAAGTTGCCTCTAAA 156 TTCAGGTCCATCGGGTTCATGC 157350 GC C VALS_EC_1105_ CGTGGCGGCGTGGTTATCGA 158 ACGAACTGGATGTCGCCGTT 1591214 16S_EC_556_ CGGAATTACTGGGCGTAAAG 160 CGCATTTCACCGCTACAC 161 700RPOC_EC_1256_ ACCCAGTGCTGCTGAACCGTGC 162 GTTCAAATGCCTGGATACCCA 163 131516S_EC_774_ GGGAGCAAACAGGATTAGATAC 164 CGTACTCCCCAGGCG 165 894RPOC_EC_1584_ TGGCCCGAAAGAAGCTGAGCG 166 ACGCGGGCATGCAGAGATGCC 167 164316S_EC_1082_ ATGTTGGGTTAAGTCCCGC 168 TGACGTCATCCCCACCTTCC 169 119616S_EC_1389_ CTTGTACACACCGCCCGTC 170 AAGGAGGTGATCCAGCC 171 154116S_EC_1303_ CGGATTGGAGTCTGCAACTCG 172 GACGGGCGGTGTGTACAAG 173 140723S_EC_23_ GGTGGATGCCTTGGC 174 GGGTTTCCCCATTCGG 175 130 23S_EC_187_GGGAACTGAAACATCTAAGTA 176 TTCGCTCGCCGCTAC 177 256 23S_EC_1602_TACCCCAAACCGACACAGG 178 CCTTCTCCCGAAGTTACG 179 1703 23S_EC_1685_CCGTAACTTCGGGAGAAGG 180 CACCGGGCAGGCGTC 181 1842 23S_EC_1827_GACGCCTGCCCGGTGC 182 CCGACAAGGAATTTCGCTACC 183 1949 23S_EC_2434_AAGGTACTCCGGGGATAACAGG 184 AGCCGACATCGAGGTGCCAAAC 185 2511 C23S_EC_2599_ GACAGTTCGGTCCCTATC 186 CCGGTCCTCTCGTACTA 187 266923S_EC_2653_ TAGTACCAGACGACCGG 188 TTAGATGCTTTCAGCACTTATC 189 275823S_BS-68_ AAACTAGATAACAGTAGACATC 190 GTGCGCCCTTTCTAACTT 191 21 AC16S_EC_8_358 AGAGTTTGATCATGGCTCAG 192 ACTGCTGCCTCCCGTAG 193 16S_EC_314_CACTGGAACTGAGACACGG 194 CTTTACGCCCAGTAATTCCG 195 575 16S_EC_518_CCAGCAGCCGCGGTAATAC 196 GTATCTAATCCTGTTTGCTCCC 197 795 16S_EC_683_GTGTAGCGGTGAAATGCG 198 GGTAAGGTTCTTCGCGTTG 199 985 16S_EC_937_AAGCGGTGGAGCATGTGG 200 ATTGTAGCACGTGTGTAGCCC 201 1240 16S_EC_1195_CAAGTCATCATGGCCCTTA 202 AAGGAGGTGATCCAGCC 203 1541 16S_EC_8_1541AGACTTTGATCATGGCTCAG 204 AAGGAGGTGATCCAGCC 205 23S_EC_1831_ACCTGCCCAGTGCTGGAAG 206 TCGCTACCTTAGGACCGT 207 1936 16S_EC_1387_GCCTTGTACACACCTCCCGTC 208 CACGGCTACCTTGTTACGAC 209 1513 16S_EC_1390_TTGTACACACCGCCCGTCATAC 210 CCTTGTTACGACTTCACCCC 211 1505 16S_EC_1367_TACGGTGAATACGTTCCCGGG 212 ACCTTGTTACGACTTCACCCCA 213 1506 16S_EC_804_ACCACGCCGTAAACGATGA 214 CCCCCGTCAATTCCTTTGAGT 215 929 16S_EC_791_GATACCCTGGTAGTCCACACCG 216 GCCTTGCGACCGTACTCCC 217 904 16S_EC_789_TAGATACCCTGGTAGTCCACGC 218 GCGACCGTACTCCCCAGG 219 899 16S_EC_1092_TAGTCCCGCAACGAGCGC 220 GACGTCATCCCCACCTTCCTCC 221 1195 23S_EC_2586_TAGAACGTCGCGAGACAGTTCG 222 AGTCCATCCCGGTCCTCTCG 223 2677 HEXAMER_EC_GAGGAAAGTCCGGGCTC 224 ATAAGCCGGGTTCTGTCG 225 61_362 RNASEP_BS_GAGGAAAGTCCATGCTCGC 226 GTAAGCCATGTTTTGTTCCATC 227 43_384 RNASEP_EC_GAGGAAAGTCCGGGCTC 228 ATAAGCCGGGTTCTGTCG 229 61_362 YAED_TRNA_GCGGGATCCTCTAGAGGTGTTA 230 GCGGGATCCTCTAGAAGACCTC 231 ALA-RRNH_AATAGCCTGGCAG CTGCGTGCAAAGC EC_513_49 RNASEP_SA_ GAGGAAAGTCCATGCTCAC 232ATAACCCATGTTCTGTTCCATC 233 31_379 16S_EC_1082_ ATGTTGGGTTAAGTCCCGC 234AAGGAGGTGATCCAGCC 235 1541 16S_EC_556_ CGGAATTACTGGGCGTAAAG 236GTATCTAATCCTGTTTGCTCCC 237 795 16S_EC_1082_ ATGTTGGGTTAAGTCCCGC 238TGACGTCATGCCCACCTTCC 239 1196_10G 16S_EC_1082_ ATGTTGGGTTAAGTCCCGC 240TGACGTCATGGCCACCTTCC 241 1196_10G_11G TRNA_ILERRNH_GCGGGATCCTCTAGACCTGATA 242 GCGGGATCCTCTAGAGCGTGAC 243 ASPRRNH_EC_AGGGTGAGGTCG AGGCAGGTATTC 32_41 16S_EC_969_ ACGCGAAGAACCTTACC 244GACGGGCGGTGTGTACAAG 245 1407 16S_EC_683_ GTGTAGCGGTGAAATGCG 246CCAGTTGCAGACTGCGATCCG 247 1323 16S_EC_49_ TAACACATGCAAGTCGAACG 248CGTACTCCCCAGGCG 249 894 16S_EC_49_ TAACACATGCAAGTCGAACG 250ACGACACGAGCTGACGAC 251 1078 CYA_BA_1349_ ACAACGAAGTACAATACAAGAC 252CTTCTACATTTTTAGCCATCAC 253 1447 16S_EC_1090_ TTAAGTCCCGCAACGAGCGCAA 254TGACGTCATCCCCACCTTCCTC 255 1196_2 16S_EC_405_ TGAGTGATGAAGGCCTTAGGGT 256CGGCTGCTGGCACGAAGTTAG 257 527 TGTAAA GROL_EC_496_ ATGGACAAGGTTGGCAAGGAAG258 TAGCCGCGGTCGAATTGCAT 259 596 G GROL_EC_511_ AAGGAAGGCGTGATCACCGTTG260 CCGCGGTCGAATTGCATGCCTT 261 593 AAGA C VALS_EC_1835_ ACGCGCTGCGCTTCAC262 TTGCAGAAGTTGCGGTAGCC 263 1928 RPOB_EC_1334_ TCGACCACCTGGGCAACC 264ATCAGGTCGTGCGGCATCA 265 1478 DNAK_EC_420_ CACGGTGCCGGCGTACT 266GCGGTCGGCTCGTTGATGAT 267 521 RPOB_EC_3776_ TTGGAGGTAAGTCTCATTTTGG 268AAGCTGCACCATAAGCTTGTAA 269 3853 TGG TGC RPOB_EC_3802_CAGCGTTTCGGCGAAATGGA 270 CGACTTGACGGTTAACATTTCC 271 3885 TGRPOB_EC_3799_ GGGCAGCGTTTCGGCGAAATGG 272 GTCCGACTTGACGGTCAACATT 273 3888A TCCTG RPOC_EC_2146_ CAGGAGTCGTTCAACTCGATCT 274 ACGCCATCAGGCCACGCAT 2752245 ACATGAT ASPS_EC_405_ GCACAACCTGCGGCTGCG 276 ACGGCACGAGGTAGTCGC 277538 RPOC_EC_1374_ CGCCGACTTCGACGGTGACC 278 GAGCATCAGCGTGCGTGCT 279 1455TUFB_EC_957_ CCACACGCCGTTCTTCAACAAC 280 GGCATCACCATTTCCTTGTCCT 281 1058T TCG 16S_EC_7_122 GAGAGTTTGATCCTGGCTCAGA 282 TGTTACTCACCCGTCTGCCACT 283ACGAA VALS_EC_610_ ACCGAGCAAGGAGACCAGC 284 TATAACGCACATCGTCAGGGTG 285727 A

For evaluation in the laboratory, five species of bacteria were selectedincluding three γ-proteobacteria (E. coli, K. pneumoniae and P.auergiosa) and two low G+C gram positive bacteria (B. subtilitis and S.aureus). The identities of the organisms were not revealed to thelaboratory technicians.

Bacteria were grown in culture, DNA was isolated and processed, and PCRperformed using standard protocols. Following PCR, all samples weredesalted, concentrated, and analyzed by Fourier Transform Ion CyclotronResonance (FTICR) mass spectrometry. Due to the extremely high precisionof the FTICR, masses could be measured to within 1 Da and unambiguouslydeconvoluted to a single base composition. The measured basecompositions were compared with the known base composition signatures inour database. As expected when using broad range survey 16S primers,several phylogenetic near-neighbor organisms were difficult todistinguish from our test organisms. Additional non-ribosomal primerswere used to triangulate and further resolve these clusters.

An example of the use of primers directed to regions of RNA polymerase B(rpoB) is shown in FIG. 19. This gene has the potential to provide broadpriming and resolving capabilities. A pair of primers directed against aconserved region of rpoB provided distinct base composition signaturesthat helped resolve the tight enterobacteriae cluster. Joint probabilityestimates of the signatures from each of the primers resulted in theidentification of a single organism that matched the identity of thetest sample. Therefore a combination of a small number of primers thatamplify selected regions of the 16S ribosomal RNA gene and a fewadditional primers that amplify selected regions of protein encodinggenes provide sufficient information to detect and identify allbacterial pathogens.

Example 16 Detection of Staphylococcus aureus in Blood Samples

Blood samples in an analysis plate were spiked with genomic DNAequivalent of 10³ organisms/ml of Staphylococcus aureus. A single set of16S rRNA primers was used for amplification. Following PCR, all sampleswere desalted, concentrated, and analyzed by Fourier Transform IonCyclotron Resonance (FTICR) mass spectrometry. In each of the spikedwells, strong signals were detected which are consistent with theexpected BCS of the S. aureus amplicon (FIG. 20). Furthermore, there wasno robotic carryover or contamination in any of the blood only or waterblank wells. Methods similar to this one will be applied for otherclinically relevant samples including, but not limited to: urine andthroat or nasal swabs.

Example 17 Detection and Serotyping of Viruses

The virus detection capability of the present invention was demonstratedin collaboration with Naval health officers using adenoviruses as anexample.

All available genomic sequences for human adenoviruses available inpublic databases were surveyed. The hexon gene was identified as acandidate likely to have broad specificity across all serotypes. Fourprimer pairs were selected from a group of primers designed to yieldbroad coverage across the majority of the adenoviral strain types (Table9) wherein Tp=5′propynylated uridine and Cp=5′propynylated cytidine.

TABLE 9 Intelligent Primer Pairs for Serotyping of Adenoviruses ForwardReverse Primer Pair Forward Primer SEQ ID Reverse Primer SEQ ID NameSequence NO: Sequence NO: HEX_HAD7 + 4 + 21_ AGACCCAATTACATTGGCTT 286CCAGTGCTGTTGTAGTACAT 287 934_995 HEX_HAD7 + 4 + 21_ ATGTACTACAACAGTACTGG288 CAAGTCAACCACAGCATTCA 289 976_1050 HEX_HAD7 + 4 + 21_GGGCTTATGTACTACAACAG 290 TCTGTCTTGCAAGTCAACCAC 291 970_1059 HEX_HAD7+ 3_771_ GGAATTTTTTGATGGTAGAGA 292 TAAAGCACAATTTCAGGCG 293 827 HEX_HAD4+ 16_ TAGATCTGGCTTTCTTTGAC 294 ATATGAGTATCTGGAGTCTGC 295 746_848HEX_HAD7_509_ GGAAAGACATTACTGCAGACA 296 CCAACTTGAGGCTCTGGCTG 297 578HEX_HAD4_1216_ ACAGACACTTACCAGGGTG 298 ACTGTGGTGTCATCTTTGTC 299 1289HEX_HAD21_515_ TCACTAAAGACAAAGGTCTTCC 300 GGCTTCGCCGTCTGTAATTTC 301 567HEX_HAD_1342_ CGGATCCAAGCTAATCTTTGG 302 GGTATGTACTCATAGGTGTTG 303 1469GTG HEX_HAD7 + 4 + 21_ AGACpCpCAATTpACpATpTGG 304 CpCpAGTGCTGTpTpGTAGTA305 934_995P CTT CAT HEX_HAD7 + 4 + 21_ ATpGTpACTpACAACAGTACpT 306CAAGTpCpAACCACAGCATpT 307 976_1050P pGG pCA HEX_HAD7 + 4 + 21_GGGCpTpTATpGTpACTACAAC 308 TCTGTpCpTTGCAAGTpCpAA 309 970_1059P pAG CCACHEX_HAD7 + 3_771_ GGAATTpTpTpTpTGATGGTAG 310 TAAAGCACAATpTpTpCpAGG 311827P AGA CG HEX_HAD4 + 16_ TAGATCTGGCTpTpTpCpTTTG 312ATATGAGTATpCpTpGGAGTp 313 746_848P AC CpTGC HEX_HAD_1342_CGGATpCCAAGCpTAATCpTpT 314 GGTATGTACTCATAGGTGTpT 315 1469P TGG pGGTGHEX_HAD7 + 21 + 3_ AACAGACCCAATTACATTGGCT 316 GAGGCACTTGTATGTGGAAAG 317931_1645 T G HEX_HAD4 + 2_925_ ATGCCTAACAGACCCAATTACA 318TTCATGTAGTCGTAGGTGTTG 319 1469 T G HEX_HAD7 + 21 + 3_CGCGCCTAATACATCTCACTGC 320 AAGCCAATGTAATTGGGTCTG 321 384_953 AT TTHEX_HAD4 + 2_345_ CTACTCTGGCACTGCCTACAAC 322 ATGTAATTGGGTCTGTTAGGC 323947 AT HEX_HAD2_772_ CAATCCGTTCTGGTTCCGGATG 324 CTTGCCGGTCGTTCAAAGAGG325 865 AA TAG HEX_HAD7 + 4 + 21_ AGTCCGGGTCTGGTGCAG 326CGGTCGGTGGTCACATC 327 73_179 HEX_HAD7 + 4 + 21_ ATGGCCACCCCATCGATG 328CTGTCCGGCGATGTGCATG 329 1_54 HEX_HAD7 + 4 + 21_ GGTCGTTATGTGCCTTTCCACA330 TCCTTTCTGAAGTTCCACTCA 331 1612_1718 T TAGG HEX_HAD7 + 4 + 21_ACAACATTGGCTACCAGGGCTT 332 CCTGCCTGCTCATAGGCTGGA 333 2276_2368 AGTT

These primers also served to clearly distinguish those strainsresponsible for most disease (types 3, 4, 7 and 21) from all others. DNAisolated from field samples known to contain adenoviruses were testedusing the hexon gene PCR primers, which provided unambiguous strainidentification for all samples. A single sample was found to contain amixture of two viral DNAs belonging to strains 7 and 21.

Test results (FIG. 21) showed perfect concordance between predicted andobserved base composition signatures for each of these samples.Classical serotyping results confirmed each of these observations.Processing of viral samples directly from collection material such asthroat swabs rather than from isolated DNA, will result in a significantincrease in throughput, eliminating the need for virus culture.

Example 18 Broad Rapid Detection and Strain Typing of RespiratoryPathogens for Epidemic Surveillance

Genome preparation: Genomic materials from culture samples or swabs wereprepared using a modified robotic protocol using DNeasy™ 96 Tissue Kit,Qiagen). Cultures of Streptococcus pyogenes were pelleted andtransferred to a 1.5 mL tube containing 0.45 g of 0.7 mm Zirconia beads(Biospec Products, Inc.). Cells were lysed by shaking for 10 minutes ata speed of 19 l/s using a MM300 Vibration Mill (Retsch, Germany). Thesamples were centrifuged for 5 min and the supernatants transferred todeep well blocks and processed using the manufacture's protocol and aQiagen 8000 BioRobot.

PCR: PCR reactions were assembled using a Packard MPII liquid handlingplatform and were performed in 50 μL volume using 1.8 units each ofPlatinum Taq (Invitrogen) and Hotstart PFU Turbo (Stratagene)polymerases. Cycling was performed on a DNA Engine Dyad (MJ Research)with cycling conditions consisting of an initial 2 min at 95° C.followed by 45 cycles of 20 s at 95° C., 15 s at 58° C., and 15 s at 72°C.

Broad-range primers: PCR primer design for base composition analysisfrom precise mass measurements is constrained by an upper limit whereionization and accurate deconvolution can be achieved. Currently, thislimit is approximately 140 base pairs. Primers designed to broadlyconserved regions of bacterial ribosomal RNAs (16 and 23S) and the geneencoding ribosomal protein L3 (rpoC) are shown in Table 10.

TABLE 10 SEQ Length Target ID of Gene Direction Primer NO Amplicon 16S_1F GGATTAGAGACCCTGGTAGTCC 334 116 16S_1 R GGCCGTACTCCCCAGGCG 335 11616S_2 F TTCGATGCAACGCGAAGAACCT 336 115 16S_2 R ACGAGCTGACGACAGCCATG 337115 23S F TCTGTCCCTAGTACGAGAGGAC 338 118 CGG 23S R TGCTTAGATGCTTTCAGC339 118 rpoC F CTGGCAGGTATGCGTGGTCTGA 340 121 TG rpoC RCGCACCGTGGGTTGAGATGAAG 341 121 TAC

Emm-typing primers: The allelic profile of a GAS strain by MultilocusSequencing Technique (MLST) can be obtained by sequencing the internalfragments of seven housekeeping genes. The nucleotide sequences for eachof these housekeeping genes, for 212 isolates of GAS (78 distinct emmtypes), are available (www.mlst.net). This corresponds to one hundreddifferent allelic profiles or unique sequence types, referred to byEnright et al. as ST1-ST100 (Enright et al., Infection and Immunity,2001, 69, 2416-2427). For each sequence type, we created a virtualtranscript by concatenating sequences appropriate to their allelicprofile from each of the seven genes. MLST primers were designed usingthese sequences and were constrained to be within each gene loci.Twenty-four primer pairs were initially designed and tested against thesequenced GAS strain 700294. A final subset of six primer pairs Table 11was chosen based on a theoretical calculation of minimal number ofprimer pairs that maximized resolution of between emm types.

TABLE 11 Drill-Down Primer Pairs Used in Determining emm-type SEQ LengthTarget ID of Gene Direction Primer NO Amplicon gki FGGGGATTCAGCCATCAAAGCAG 342 116 CTATTGAC gki R CCAACCTTTTCCACAACAGAAT 343116 CAGC gtr F CCTTACTTCGAACTATGAATCT 344 115 TTTGGAAG gtr RCCCATTTTTTCACGCATGCTGA 345 115 AAATATC murI F CGCAAAAAAATCCAGCTATTAG 346118 C murI R AAACTATTTTTTTAGCTATACT 347 118 CGAACAC mutS FATGATTACAATTCAAGAAGGTC 348 121 GTCACGC mutS R TTGGACCTGTAATCAGCTGAAT 349121 ACTGG xpt F GATGACTTTTTAGCTAATGGTC 350 122 AGGCAGC xpt RAATCGACGACCATCTTGGAAAG 351 122 ATTTCTC yqiL F GCTTCAGGAATCAATGATGGAG 352119 CAG yqiL R GGGTCTACACCTGCACTTGCAT 353 119 AAC

Microbiology: GAS isolates were identified from swabs on the basis ofcolony morphology and beta-hemolysis on blood agar plates, gram staincharacteristics, susceptibility to bacitracin, and positive latexagglutination reactivity with group A-specific antiserum.

Sequencing: Bacterial genomic DNA samples of all isolates were extractedfrom freshly grown GAS strains by using QIAamp DNA Blood Mini Kit(Qiagen, Valencia, Calif.) according to the procedures described by themanufacture. Group A streptococcal cells were subjected to PCR andsequence analysis using emm-gene specific PCR as previously described(Beall et al., J. Clin. Micro., 1996, 34, 953-958; and Facklam et al.,Emerg. Infect. Dis., 1999, 5, 247-253). Homology searches on DNAsequences were conducted against known emm sequences present in(www.cdc.gov/ncidod/biotech/infotech_hp.html). For MLST analysis,internal fragments of seven housekeeping genes, were amplified by PCRand analyzed as previously described (Enright et al., Infection andImmunity 2001, 69, 2416-2427). The emm-type was determined fromcomparison to the MLST database.

Broad Range Survey/Drill-Down Process (100): For Streptococcus pyogenes,the objective was the identification of a signature of the virulentepidemic strain and determination of its emm-type. Emm-type informationis useful both for treatment considerations and epidemic surveillance. Atotal of 51 throat swabs were taken both from healthy recruits and fromhospitalized patients in December 2002, during the peak of a GASoutbreak at a military training camp. Twenty-seven additional isolatesfrom previous infections ascribed to GAS were also examined. Initially,isolated colonies were examined both from throat culture samples andthroat swabs directly without the culture step. The latter path can becompleted within 6-12 hours providing information on a significantnumber of samples rapidly enough to be useful in managing an ongoingepidemic.

The process of broad range survey/drill-down (200) is shown in FIG. 22.A clinical sample such as a throat swab is first obtained from anindividual (201). Broad range survey primers are used to obtainamplification products from the clinical sample (202) which are analyzedto determine a BCS (203) from which a species is identified (204).Drill-down primers are then employed to obtain PCR products (205) fromwhich specific information is obtained about the species (such asEmm-type) (206).

Broad Range Survey Priming: Genomic regions targeted by the broad rangesurvey primers were selected for their ability to allow amplification ofvirtually all known species of bacteria and for their capability todistinguish bacterial species from each other by base compositionanalysis. Initially, four broad-range PCR target sites were selected andthe primers were synthesized and tested. The targets includeduniversally conserved regions of 16S and 23S rRNA, and the gene encodingribosomal protein L3 (rpoC).

While there was no special consideration of Streptococcus pyogenes inthe selection of the broad range survey primers (which were optimizedfor distinguishing all important pathogens from each other), analysis ofgenomic sequences showed that the base compositions of these regionsdistinguished Streptococcus pyogenes from other respiratory pathogensand normal flora, including closely related species of streptococci,staphylococci, and bacilli (FIG. 23).

Drill Down Priming (Emm-Typing). In order to obtain strain-specificinformation about the epidemic, a strategy was designed to measure thebase compositions of a set of fast clock target genes to generatestrain-specific signatures and simultaneously correlate with emm-types.In classic MLST analysis, internal fragments of seven housekeeping genes(gki, gtr, murI, mutS, recP, xpt, yqiL) are amplified, sequenced andcompared to a database of previously studied isolates whose emm-typeshave been determined (Horner et al. Fundamental and Applied Toxicology,1997, 36, 147). Since the analysis enabled by the present embodiment ofthe present invention provides base composition data rather thansequence data, the challenge was to identify the target regions thatprovide the highest resolution of species and least ambiguousemm-classification. The data set from Table 2 of Enright et al. (Enrightet al. Infection and Immunity, 2001, 69, 2416-2427) to bioinformaticallyconstruct an alignment of concatenated alleles of the seven housekeepinggenes from each of 212 previously emm-typed strains, of which 101 wereunique sequences that represented 75 distinct emm-types. This alignmentwas then analyzed to determine the number and location of the optimalprimer pairs that would maximize strain discrimination strictly on basecomposition data.

An example of assignment of BCSs of PCR products is shown in FIG. 24where PCR products obtained using the gtr primer (a drill-downemm-typing primer) from two different swab samples were analyzed (sample12—top and sample 10—bottom). The deconvoluted ESI-FCTIR spectra provideaccurate mass measurements of both strands of the PCR products, fromwhich a series of candidate BCSs were calculated from the measured mass(and within the measured mass uncertainty). The identification ofcomplementary candidate BCSs from each strand provides a means forunambiguous assignment of the BCS of the PCR product. BCSs and molecularmasses for each strand of the PCR product from the two different samplesare also shown in FIG. 24. In this case, the determination of BCSs forthe two samples resulted in the identification of the emm-type ofStreptococcus pyogenes—sample 12 was identified as emm-type 3 and sample10 was identified as emm-type 6.

The results of the composition analysis using the six primer pairs,5′-emm gene sequencing and MLST gene sequencing method for the GASepidemic at a military training facility are compared in FIG. 25. Thebase composition results for the six primer pairs showed a perfectconcordance with 5′-emm gene sequencing and MLST sequencing methods. Ofthe 51 samples taken during the peak of the epidemic, all but three hadidentical compositions and corresponded to emm-type 3. The threeoutliers, all from healthy individuals, probably represent non-epidemicstrains harbored by asymptomatic carriers. Samples 52-80, which werearchived from previous infections from Marines at other naval trainingfacilities, showed a much greater heterogeneity of compositionsignatures and emm-types.

Example 19 Base Composition Probability Clouds

FIG. 18 illustrates the concept of base composition probability cloudsvia a pseudo-four dimensional plot of base compositions ofenterobacteria including Y. pestis, Y. psuedotuberculosis, S.typhimurium, S. typhi, Y. enterocolitica, E. coli K12, and E. coliO157:H7. In the plot of FIG. 18, A, C and G compositions correspond tothe x, y and z axes respectively whereas T compositions are representedby the size of the sphere at the junction of the x, y and z coordinates.There is no absolute requirement for having a particular nucleobasecomposition associated with a particular axis. For example, a plot couldbe designed wherein G, T and C compositions correspond to the x, y and zaxes respectively whereas the A composition corresponds to the size ofthe sphere at the junction of the x, y and z coordinates. Furthermore, adifferent representation can be made of the “pseudo fourth” dimensioni.e.: other than the size of the sphere at junction of the x, y and zcoordinates. For example, a symbol having vector information such as anarrow or a cone can be rotated at an angle that varies proportionallywith the composition of the nucleobase corresponding to the pseudofourth dimension. The choice of axes and pseudo fourth dimensionalrepresentation is typically made with the aim of optimal visualizationof the data being presented.

A similar base composition probability cloud analysis has been presentedfor a series of viruses in U.S. provisional patent application Ser. No.60/431,319, which is commonly owned and incorporated herein by referencein its entirety. In this base composition probability cloud analysis,the closely related Dengue virus types 1-4 are clearly distinguishablefrom each other. This example is indicative of a challenging scenariofor species identification based on BCS analysis because RNA viruseshave a high mutation rate, it would be expected to be difficult toresolve closely related species. However, as this example illustrates,BCS analysis, aided by base composition probability cloud analysis iscapable of resolution of closely related viral species.

A base composition probability cloud can also be represented as a threedimensional plot instead of a pseudo-four dimensional plot. An exampleof such a three dimensional plot is a plot of G, A and C compositionscorrespond to the x, y and z axes respectively, while the composition ofT is left out of the plot. Another such example is a plot where thecompositions of all four nucleobases is included: G, A and C+Tcompositions correspond to the x, y and z axes respectively. As for thepseudo-four dimensional plots, the choice of axes for a threedimensional plot is typically made with the aim of optimal visualizationof the data being presented.

Example 20 Biochemical Processing of Large Amplification Products forAnalysis by Mass Spectrometry

In the example illustrated in FIG. 26, a primer pair which amplifies a986 bp region of the 16S ribosomal gene in E. coli (K12) was digestedwith a mixture of 4 restriction enzymes: BstN1, BsmF1, Bfa1, and Nco1.FIG. 26( a) illustrates the complexity of the resulting ESI-FTICR massspectrum that contains multiple charge states of multiple restrictionfragments. Upon mass deconvolution to neutral mass, the spectrum issignificantly simplified and discrete oligonucleotide pairs are evident(FIG. 26 b). When base compositions are derived from the masses of therestriction fragments, perfect agreement is observed for the knownsequence of nucleotides 1-856 (FIG. 26 c); the batch of Nco1 enzyme usedin this experiment was inactive and resulted in a missed cleavage siteand a 197-mer fragment went undetected as it is outside the mass rangeof the mass spectrometer under the conditions employed. Interestinglyhowever, both a forward and reverse strand were detected for eachfragment measured (solid and dotted lines in, respectively) within 2 ppmof the predicted molecular weights resulting in unambiguousdetermination of the base composition of 788 nucleotides of the 985nucleotides in the amplicon. The coverage map offers redundant coverageas both 5′ to 3′ and 3′ to 5′ fragments are detected for fragmentscovering the first 856 nucleotides of the amplicon.

This approach is in many ways analogous to those widely used in MS-basedproteomics studies in which large intact proteins are digested withtrypsin, or other proteolytic enzyme(s), and the identity of the proteinis derived by comparing the measured masses of the tryptic peptides withtheoretical digests. A unique feature of this approach is that theprecise mass measurements of the complementary strands of each digestproduct allow one to derive a de novo base composition for eachfragment, which can in turn be “stitched together” to derive a completebase composition for the larger amplicon. An important distinctionbetween this approach and a gel-based restriction mapping strategy isthat, in addition to determination of the length of each fragment, anunambiguous base composition of each restriction fragment is derived.Thus, a single base substitution within a fragment (which would not beresolved on a gel) is readily observed using this approach. Because thisstudy was performed on a 7 Tesla ESI-FTICR mass spectrometer, betterthan 2 ppm mass measurement accuracy was obtained for all fragments.Interestingly, calculation of the mass measurement accuracy required toderive unambiguous base compositions from the complementary fragmentsindicates that the highest mass measurement accuracy actually requiredis only 15 ppm for the 139 bp fragment (nucleotides 525-663). Most ofthe fragments were in the 50-70 bp size-range which would require massaccuracy of only ˜50 ppm for unambiguous base composition determination.This level of performance is achievable on other more compact, lessexpensive MS platforms such as the ESI-TOF suggesting that the methodsdeveloped here could be widely deployed in a variety of diagnostic andhuman forensic arenas.

This example illustrates an alternative approach to derive basecompositions from larger PCR products. Because the amplicons of interestcover many strain variants, for some of which complete sequences are notknown, each amplicon can be digested under several different enzymaticconditions to ensure that a diagnostically informative region of theamplicon is not obscured by a “blind spot” which arises from a mutationin a restriction site. The extent of redundancy required to confidentlymap the base composition of amplicons from different markers, anddetermine which set of restriction enzymes should be employed and howthey are most effectively used as mixtures can be determined. Theseparameters will be dictated by the extent to which the area of interestis conserved across the amplified region, the compatibility of thevarious restriction enzymes with respect to digestion protocol (buffer,temperature, time) and the degree of coverage required to discriminateone amplicon from another.

Example 21 Identification of members of the Viral Genus Orthopoxvirus

Primer sites were identified on three essential viral genes—theDNA-dependent polymerase (DdDp), and two sub-units of DNA-dependent RNApolymerases A and B (DdRpA and DdRpB). These intelligent primersdesigned to identify members of the viral genus Orthopoxvirus are shownin Table 12 wherein Tp=5′propynylated uridine and Cp=5′propynylatedcytidine.

TABLE 12 Intelligent Primer Pairs for Identification of members of theViral Genus Orthopoxvirus Forward Reverse Primer Pair Forward Primer SEQID Reverse Primer SEQ ID Name Sequence NO: Sequence NO: A25L_NC001611_GTACTGAATCCGCCTAAG 354 GTGAATAAAGTATCGCCCTAA 355 28_127 TAA18R_NC001611_ GAAGTTGAACCGGGATCA 356 ATTATCGGTCGTTGTTAATGT 357 100_207A18R_NC001611_ CTGTCTGTAGATAAACTAGGAT 358 CGTTCTTCTCTGGAGGAT 3591348_1445 T E9L_N0001611_ CGATACTACGGACGC 360 CTTTATGAATTACTTTACATA 3611119_1222 T K8R_NC001611_ CTCCTCCATCACTAGGAA 362 CTATAACATTCAAAGCTTATT363 221_311 G A24R_NC001611_ CGCGATAATAGATAGTGCTAAA 364GCTTCCACCAGGTCATTAA 365 795_878 C A25L_NC001611_ GTACpTpGAATpCpCpGCpCpT366 GTGAATAAAGTATpCpGCpCp 367 28_127P AAG CpTpAATA A18R_NC001611_GAAGTpTpGAACpCpGGGATCA 368 ATTATCGGTpCpGTpTpGTpT 369 100_207P pAATGTA18R_NC001611_ CTGTpCpTpGTAGATAAACpTp 370 CGTTCpTpTpCpTpCpTpGGA 3711348_1445P AGGATT GGAT E9L_NC001611_ CGATACpTpACpGGACGC 372CTTTATGAATpTpACpTpTpT 373 1119_1222P pACATAT K8R_NC001611_CTpCpCpTCpCpATCACpTpAG 374 CTATAACATpTpCpAAAGCpT 375 221_311P GAApTpATTG A24R_NC001611_ CGCGATpAATpAGATAGTpGCp 376 GCTTCpCpACpCAGGTpCATp377 795_878P TpAAAC TAA

As illustrated in FIG. 27, members of the Orthopoxvirus genus group canbe identified, distinguished from one another, and distinguished fromother members of the Poxvirus family using a single pair of primersdesigned against the DdRpB gene.

Since the primers were designed across regions of high conservationwithin this genus, the likelihood of missed detection due to sequencevariations at these sites is minimized. Further, none of the primers isexpected to amplify other viruses or any other DNA, based on the dataavailable in GenBank. This method can be used for all families of viralthreat agents and is not limited to members of the Orthopoxvirus genus.

Example 22 Identification of Viruses that Cause Viral Hemorrhagic Fevers

In accordance with the present invention an approach of broad PCRpriming across several different viral species is employed usingconserved regions in the various viral genomes, amplifying a small, yethighly informative region in these organisms, and then analyzing theresultant amplicons with mass spectrometry and data analysis. Theseregions will be tested with live agents, or with genomic constructsthereof.

Detection of RNA viruses will necessitate a reverse transcription (RT)step prior to the PCR amplification of the TIGER reporter amplicon. Tomaximize throughput and yield while minimizing the handling of thesamples, commercial one-step reverse transcription polymerase chainreaction (RT-PCR) kits will be evaluated for use. If necessary, aone-step RT-PCR mix using our selected DNA polymerase for the PCRportion of the reaction will be developed. To assure there is novariation in our reagent performance all new lots of enzymes,nucleotides and buffers will be individually tested prior to use.

Various modifications of the invention, in addition to those describedherein, will be apparent to those skilled in the art from the foregoingdescription. Such modifications are also intended to fall within thescope of the appended claims. Each reference, web site, Genebankaccession number, etc. cited in the present application is incorporatedherein by reference in its entirety.

1.-49. (canceled)
 50. A method of differentiating two or more bioagents,comprising: a) contacting nucleic acid from two or more bioagents withtwo or more oligonucleotide primers that hybridize to sequence regionsof said nucleic acid from said two or more bioagents that are conservedamong different bioagents, wherein said conserved sequence regions flanka variable sequence region to produce two or more amplificationproducts; b) determining two or more base compositions of said two ormore amplification products; and c) differentiating said two or morebioagents by comparing said determined two or more base compositions toa database of base compositions from a plurality of different bioagents.51. The method of claim 50, wherein at least one of said two or morebioagents is selected from the group of bioagents consisting of a viralbioagent, a bacterial bioagent, a fungal bioagent, a protozoal bioagent,a parasitic bioagent, a mammalian bioagent, and a human bioagents. 52.The method of claim 50, wherein at least one of said two or morebioagents is selected from the group of bioagents consisting of anevolving bioagent, a mutating bioagent, a recombinant bioagent, and anengineered bioagent.
 53. The method of claim 50, wherein at least one ofsaid two or more bioagents is not previously known to exist.
 54. Themethod of claim 50, wherein said two or more bioagents aredifferentiated in a sample.
 55. The method of claim 54, wherein saidsample is selected from the group consisting of a body fluid sample, anenvironmental sample, a culture media sample, a manufacturing sample,and a forensic sample.
 56. The method of claim 54, wherein said samplecomprises two or more bioagents of different genus, and wherein saidnucleic acid from said sample is contacted with two or moreoligonucleotide primers configured to differentiate two or morebioagents of different genus.
 57. The method of claim 54, wherein saidsample comprises two or more bioagents of different species, and whereinsaid nucleic acid from said sample is contacted with two or moreoligonucleotide primers configured to differentiate two or morebioagents of different species.
 58. The method of claim 50, furthercomprising purifying said nucleic acid prior to production of saidamplification product.
 59. The method of claim 50, further comprisingmodifying said nucleic acid prior to production of said two or moreamplification products.
 60. The method of claim 50, wherein said nucleicacid is selected from the group consisting of genomic DNA, RNA, DNA thatis complementary to RNA, DNA that is synthesized from RNA,double-stranded DNA, single stranded DNA, DNA that is the product ofamplification, DNA that is fragmented, nuclear DNA, mitochondrial DNA,cytoplasmic DNA and extracellular DNA.
 61. The method of claim 50,wherein said two or more oligonucleotide primers are about 15-35nucleobases in length, and wherein said two or more oligonucleotideprimers comprise at least 70%, at least 80%, at least 90%, at least 95%,or at least 100% sequence identify with said conserved sequence regions.62. The method of claim 50, wherein at least one of said two or moreamplification products is 45 to 200 nucleobases in length.
 63. Themethod of claim 50, wherein a non-templated T residue on the 5′-end ofat least one of said two or more oligionucleotides primers is removed.64. The method of claim 50, wherein at least one of said two or moreoligonucleotide primers comprises a non-templated T residue on the5′-end.
 65. The method of claim 50, wherein at least one of said two ormore oligonucleotide primers comprises at least one molecular massmodifying tag.
 66. The method of claim 50, wherein at least one of saidtwo or more oligonucleotide primers comprises at least one modifiednucleobase.
 67. The method of claim 66, wherein said modified nucleobaseis 5-propynyluracil or 5-propynylcytosine.
 68. The method of claim 66,wherein said modified nucleobase is a mass modified nucleobase.
 69. Themethod of claim 68, wherein said mass modified nucleobase is 5-Iodo-C.70. The method of claim 66, wherein said modified nucleobase is auniversal nucleobase.
 71. The method of claim 70, wherein said universalnucleobase is inosine.
 72. The method of claim 50, wherein said two ormore bioagents are differentiated at the genus, species, sub-species,strain, sub-type, or nucleotide polymorphism levels.
 73. The method ofclaim 50, wherein said two or more oligonucleotide primers comprisemultiple oligonucleotide primer sets configured for differentiation ofdiverse bioagents.
 74. The method of claim 50, wherein said conservedsequence regions are within a gene.
 75. The method of claim 50, whereinsaid conserved sequence regions are within a coding region of a gene.76. The method of claim 50, wherein said conserved sequence regions arewithin a regulatory region of a gene.
 77. The method of claim 50,wherein said variable sequence region is within a gene.
 78. The methodof claim 50, wherein said variable sequence region is within a codingregion of a gene.
 79. The method of claim 50, wherein said variablesequence region is within a regulatory region of a gene.
 80. The methodof claim 50, wherein said conserved sequence regions are about 10 to 100nucleobases in length.
 81. The method of claim 50, wherein said variablesequence region is about 10 to 200 nucleobases in length.
 82. The methodof claim 50, wherein said amplification product is produced bypolymerase chain reaction.
 83. The method of claim 50, furthercomprising purifying said amplification product.
 84. The method of claim50, wherein said base composition is determined by mass spectrometry.85. The method of claim 84, wherein said mass spectrometry is ESI massspectrometry.
 86. The method of claim 50, wherein said base compositionof said amplification product comprises identification of the number ofA residues, C residues, T residues, G residues, U residues, analogsthereof and/or mass tag residues thereof in said amplification product.87. The method of claim 50, wherein said base composition is determinedwithout sequencing said amplification product.
 88. The method of claim50, wherein said comparing comprises identifying a match between saiddetermined two or more base compositions and at least one entry withinsaid database of base compositions from a plurality of differentbioagents.
 89. The method of claim 50, wherein said differentiating saidtwo or more bioagents requires two or more oligonucleotide primer pairs.90. The method of claim 50, wherein said database of base compositionsfrom a plurality of different bioagents comprises base compositions ofgenus specific amplification products, family specific amplificationproducts, species specific amplification products, strain specificamplification products, sub-type specific amplification products, ornucleotide polymorphism specific amplification products produced withsaid two or more oligonucleotide primers, wherein one or more matchesbetween said determined base composition of said amplification productand one or more entries in said database identifies said two or morebioagents, classifies a major classification of said two or morebioagents, or differentiates between subgroups of known and unknown saidtwo or more bioagents.
 91. The method of claim 50, wherein said databaseof base compositions comprises base composition information for at least3 different bioagents.
 92. The method of claim 50, wherein said databaseof base compositions comprises base composition information for at least4 different bioagents.
 93. The method of claim 50, wherein said databaseof base compositions comprises base composition information for at least8 different bioagents.
 94. The method of claim 50, wherein said databaseof base compositions comprises base composition information for at least19 different bioagents.
 95. The method of claim 50, wherein saiddatabase of base compositions comprises base composition information forat least 30 different bioagents.
 96. The method of claim 50, whereinsaid database of base compositions comprises at least 12 unique basecompositions.
 97. The method of claim 50, wherein said database of basecompositions comprises at least 40 unique base compositions.
 98. Themethod of claim 50, wherein said database of base compositions comprisesbase composition information for a bioagent from two or more genusesselected from the group consisting of Acinetobacter, Aeromonas,Bacillus, Bacteroides, Bartonella, Bordetella, Borrelia, Brucella,Burkholderia, Campylobacter, Chlamydia, Chlamydophila, Clostridium,Coxiella, Enterococcus, Escherichia, Francisella, Fusobacterium,Haemophilus, Helicobacter, Klebsiella, Legionella, Leptospira, Listeria,Moraxella, Mycobacterium, Mycoplasma, Neisseria, Proteus, Pseudomonas,Rhodobacter, Rickettsia, Salmonella, Shigella, Staphylococcus,Streptobacillus, Streptomyces, Treponema, Ureaplasma, Vibrio, orYersinia.
 99. The method of claim 50, wherein said database of basecompositions comprises base composition information for a bioagent fromeach of the genuses of Acinetobacter, Aeromonas, Bacillus, Bacteroides,Bartonella, Bordetella, Borrelia, Brucella, Burkholderia, Campylobacter,Chlamydia, Chlamydophila, Clostridium, Coxiella, Enterococcus,Escherichia, Francisella, Fusobacterium, Haemophilus, Helicobacter,Klebsiella, Legionella, Leptospira, Listeria, Moraxella, Mycobacterium,Mycoplasma, Neisseria, Proteus, Pseudomonas, Rhodobacter, Rickettsia,Salmonella, Shigella, Staphylococcus, Streptobacillus, Streptomyces,Treponema, Ureaplasma, Vibrio, or Yersinia.
 100. The method of claim 50,wherein said database of base compositions comprises base compositioninformation for a bioagent from two or more orders or families selectedfrom the group consisting of Smallpox virus, Arenavirus, Arenavirus,Mononegavirales, Picornaviruses, Astroviruses, Astroviruses,Nidovirales, Flaviviruses, and Togaviruses.
 101. The method of claim 50,wherein said database of base compositions comprises base compositioninformation for a bioagent from each of the orders or families ofSmallpox virus, Arenavirus, Bunyaviruses, Mononegavirales,Picornaviruses, Astroviruses, Calciviruses, Nidovirales, Flaviviruses,and Togaviruses.
 102. The method of claim 50, wherein said basecompositions in said database are associated with bioagent identity.103. The method of claim 50, wherein said base compositions in saiddatabase are associated with bioagent geographic origin.
 104. The methodof claim 50, wherein said comparing step is performed by a computer.105. The method of claim 104, wherein said computer identifies a matchbetween said determined base composition of said amplification productand one or more entries in database of base compositions from aplurality of different bioagents with a probability algorithm.
 106. Themethod of claim 50, wherein said database is stored on a computer. 107.The method of claim 106, wherein said computer is a local computer. 108.The method of claim 106, wherein said computer is a remote computer.109. The method of claim 50, wherein said differentiating said two ormore bioagents comprises interrogation of said database with two or moredifferent base compositions associated with said two or more bioagents.110. The method of claim 50, wherein said database of base compositionscomprises at least 10 base compositions.
 111. The method of claim 50,wherein said database of base compositions comprises at least 20 basecompositions.
 112. The method of claim 50, wherein said database of basecompositions comprises at least 30 base compositions.
 113. The method ofclaim 50, wherein said database of base compositions comprises at least40 base compositions.
 114. The method of claim 50, wherein said databaseof base compositions comprises at least 50 base compositions.
 115. Themethod of claim 50, wherein said database of base compositions comprisesat least 60 base compositions.
 116. The method of claim 50, wherein saiddatabase of base compositions comprises at least 70 base compositions.117. The method of claim 50, wherein said database of base compositionscomprises at least 80 base compositions.
 118. The method of claim 50,wherein said database of base compositions comprises at least 90 basecompositions.
 119. The method of claim 50, wherein said database of basecompositions comprises at least 100 base compositions.
 120. The methodof claim 50, wherein said database of base compositions comprises atleast 500 base compositions.
 121. The method of claim 50, wherein saiddatabase of base compositions comprises at least 1000 base compositions.122. The method of claim 50, wherein said two or more bioagentscomprises at least 10, at least 20, at least 30, at least 40, at least50, at least 60, at least 70, at least 80, at least 90, at least 100, atleast 500 or at least 1000 bioagents.
 123. The method of claim 50,wherein said two or more bioagents are differentiated by kingdom,phylum, class, order, family, genus, species, sub-species, strain,sub-type, or nucleotide polymorphism.
 124. A system, comprising: a) anucleic acid amplification component; b) a base compositiondetermination component; c) a base composition identification componentcomprising a database of base compositions from a plurality ofbioagents; and d) a processor configured to compare two or more basecompositions produced by said base composition determination componentwith said database to differentiate two or bioagents.
 125. The system ofclaim 124, wherein said two or more base compositions are derived fromtwo or more bioagents.
 126. The system of claim 124, further comprisinga nucleic acid purification component.
 127. The system of claim 126,wherein said nucleic acid purification component comprises one or morebuffer manipulations, one or more salt manipulations, one or morethermal manipulations, one or more pH manipulations, one or moremechanical manipulations, one or more centrifugation manipulations, orone or more magnetic manipulations.
 128. The system of claim 124,wherein said nucleic acid amplification component comprises athermocycler.
 129. The system of claim 124, wherein said nucleic acidamplification component comprises one or more salts, one or morebuffers, one or more purified oligonucleotide primers, one or moredNTPs, or one or more enzymes.
 130. The system of claim 124, whereinsaid base composition identification component comprises a massspectrometer.
 131. The system of claim 130, wherein said massspectrometer is an ESI mass spectrometer.
 132. The system of claim 124,further comprising a computer program on a computer readable mediumconfigured to direct said processor to coordinate the operation of saidnucleic acid component, said base composition determination component,and said base composition identification component.
 133. The system ofclaim 124, wherein said processor is configured to process massspectrometry data to base composition data.
 134. The system of claim124, wherein said database of base compositions comprises at least 10base compositions.
 135. The system of claim 124, wherein said databaseof base compositions comprises at least 20 base compositions.
 136. Thesystem of claim 124, wherein said database of base compositionscomprises at least 30 base compositions.
 137. The system of claim 124,wherein said database of base compositions comprises at least 40 basecompositions.
 138. The system of claim 124, wherein said database ofbase compositions comprises at least 50 base compositions.
 139. Thesystem of claim 124, wherein said database of base compositionscomprises at least 60 base compositions.
 140. The system of claim 124,wherein said database of base compositions comprises at least 70 basecompositions.
 141. The system of claim 124, wherein said database ofbase compositions comprises at least 80 base compositions.
 142. Thesystem of claim 124, wherein said database of base compositionscomprises at least 90 base compositions.
 143. The system of claim 124,wherein said database of base compositions comprises at least 100 basecompositions.
 144. The system of claim 124, wherein said database ofbase compositions comprises at least 500 base compositions.
 145. Thesystem of claim 124, wherein said database of base compositionscomprises at least 1000 base compositions.
 146. The system of claim 124,wherein said plurality of bioagents comprises bioagents that differ bykingdom, phylum, class, order, family, genus, species, sub-species,strain, sub-type, or nucleotide polymorphism.
 147. The system of claim124, wherein said plurality of bioagents comprises one or more viralbioagents, one or more bacterial bioagents, one or more fungalbioagents, one or more protozoal bioagents, one or more parasiticbioagents, one or more mammalian bioagents, or one or more humanbioagents.